Overview

Dataset statistics

Number of variables331
Number of observations533106
Missing cells78923935
Missing cells (%)44.7%
Total size in memory1.3 GiB
Average record size in memory2.6 KiB

Variable types

DateTime1
Numeric1
Categorical327
Unsupported2

Alerts

UmfrageName has constant value "kuzu_zug" Constant
BHF_park has a high cardinality: 127 distinct values High cardinality
BHF_platz has a high cardinality: 399 distinct values High cardinality
BHF_sauberkeit has a high cardinality: 481 distinct values High cardinality
BHF_umsteig has a high cardinality: 531 distinct values High cardinality
BHF_velo has a high cardinality: 149 distinct values High cardinality
BHF_wc has a high cardinality: 65 distinct values High cardinality
BHF_wegweisung has a high cardinality: 420 distinct values High cardinality
createdAt has a high cardinality: 447792 distinct values High cardinality
date_of_first_mail has a high cardinality: 43568 distinct values High cardinality
date_of_last_access has a high cardinality: 364966 distinct values High cardinality
EKL_fehlende_infos_txt has a high cardinality: 693 distinct values High cardinality
fg_abfahrt has a high cardinality: 1399 distinct values High cardinality
fg_ankunft has a high cardinality: 1415 distinct values High cardinality
fg_startort has a high cardinality: 15985 distinct values High cardinality
fg_startort_uic has a high cardinality: 14300 distinct values High cardinality
fg_via has a high cardinality: 49633 distinct values High cardinality
fg_vm has a high cardinality: 153564 distinct values High cardinality
fg_zielort has a high cardinality: 15776 distinct values High cardinality
fg_zielort_uic has a high cardinality: 14065 distinct values High cardinality
ft_abfahrt has a high cardinality: 1394 distinct values High cardinality
ft_ankunft has a high cardinality: 1406 distinct values High cardinality
ft_haltestellen has a high cardinality: 25028 distinct values High cardinality
ft_startort has a high cardinality: 7595 distinct values High cardinality
ft_startort_uic has a high cardinality: 2837 distinct values High cardinality
ft_tu has a high cardinality: 60 distinct values High cardinality
ft_uic_haltestellen has a high cardinality: 33100 distinct values High cardinality
ft_vm has a high cardinality: 30662 distinct values High cardinality
ft_zielort has a high cardinality: 6546 distinct values High cardinality
ft_zielort_uic has a high cardinality: 1835 distinct values High cardinality
ft_zug_nr has a high cardinality: 19555 distinct values High cardinality
Kommentar has a high cardinality: 143382 distinct values High cardinality
OES_grund_beeintraecht_other_txt has a high cardinality: 3643 distinct values High cardinality
OES_grund_personal_negativ_txt has a high cardinality: 874 distinct values High cardinality
participant_id has a high cardinality: 533106 distinct values High cardinality
projectLfn has a high cardinality: 224271 distinct values High cardinality
R_grund_nonuse_5txt has a high cardinality: 1930 distinct values High cardinality
R_Park has a high cardinality: 262 distinct values High cardinality
R_platz_andere_txt has a high cardinality: 581 distinct values High cardinality
R_sauber_anderes_txt has a high cardinality: 272 distinct values High cardinality
R_umsteig_andere_txt has a high cardinality: 1101 distinct values High cardinality
R_unzuf_comfort_txt has a high cardinality: 16499 distinct values High cardinality
R_unzuf_fahrplan_txt has a high cardinality: 6194 distinct values High cardinality
R_unzuf_gastro_ambiente_txt has a high cardinality: 144 distinct values High cardinality
R_unzuf_gastro_auswahl_txt has a high cardinality: 175 distinct values High cardinality
R_unzuf_gastro_kompetenz_txt has a high cardinality: 68 distinct values High cardinality
R_unzuf_gastro_preis_txt has a high cardinality: 249 distinct values High cardinality
R_unzuf_info_txt has a high cardinality: 3333 distinct values High cardinality
R_unzuf_mobile_txt has a high cardinality: 5197 distinct values High cardinality
R_unzuf_platzangebot_txt has a high cardinality: 8809 distinct values High cardinality
R_unzuf_preis_txt has a high cardinality: 12697 distinct values High cardinality
R_unzuf_gastro_quality_txt has a high cardinality: 87 distinct values High cardinality
R_unzuf_puenktlichkeit_txt has a high cardinality: 5552 distinct values High cardinality
R_unzuf_Sauberkeit_Bhf_txt has a high cardinality: 2556 distinct values High cardinality
R_unzuf_stoerungsinfo_txt has a high cardinality: 3767 distinct values High cardinality
R_unzuf_wc_avail_txt has a high cardinality: 6012 distinct values High cardinality
R_unzuf_wc_clean_txt has a high cardinality: 4432 distinct values High cardinality
R_unzuf_Wegweisung_Bhf_txt has a high cardinality: 2420 distinct values High cardinality
R_unzuf_zug_clean_txt has a high cardinality: 10918 distinct values High cardinality
R_unzuf_zugpers_txt has a high cardinality: 3154 distinct values High cardinality
R_Velo has a high cardinality: 584 distinct values High cardinality
R_WC has a high cardinality: 103 distinct values High cardinality
R_weg_andere_txt has a high cardinality: 612 distinct values High cardinality
RF_kanal_other_txt has a high cardinality: 2241 distinct values High cardinality
RF_mob_andere_txt has a high cardinality: 405 distinct values High cardinality
RF_webs_andere_txt has a high cardinality: 67 distinct values High cardinality
RF_zufallsitem1_label has a high cardinality: 696 distinct values High cardinality
S_AB7txt has a high cardinality: 4719 distinct values High cardinality
S_alter has a high cardinality: 91 distinct values High cardinality
SF_kanal_other_txt has a high cardinality: 1721 distinct values High cardinality
tag_zug_nr has a high cardinality: 406662 distinct values High cardinality
u_artikel has a high cardinality: 246 distinct values High cardinality
SF_unzuf_info_txt has a high cardinality: 1404 distinct values High cardinality
u_date has a high cardinality: 2063 distinct values High cardinality
u_hindatum has a high cardinality: 2063 distinct values High cardinality
u_kaufdatum has a high cardinality: 2110 distinct values High cardinality
u_preis has a high cardinality: 2839 distinct values High cardinality
updatedAt has a high cardinality: 447792 distinct values High cardinality
wime_unzuf_sf_txt has a high cardinality: 1470 distinct values High cardinality
SF_kanal_zuf has 367364 (68.9%) missing values Missing
wime_kundenorientierung has 367364 (68.9%) missing values Missing
wime_mobile has 367364 (68.9%) missing values Missing
Wime_Sauberkeit_BhfZiel has 328381 (61.6%) missing values Missing
Wime_Sauberkeit_BhfStart has 328381 (61.6%) missing values Missing
wime_sf_behebung has 367364 (68.9%) missing values Missing
Wime_Wegweisung_BhfZiel has 328381 (61.6%) missing values Missing
Wime_Wegweisung_BhfStart has 328381 (61.6%) missing values Missing
BFH_sahre_non has 437163 (82.0%) missing values Missing
BFH_sahre_start has 437163 (82.0%) missing values Missing
BFH_sahre_ziel has 437163 (82.0%) missing values Missing
BHF_park has 437162 (82.0%) missing values Missing
BHF_park_non has 437163 (82.0%) missing values Missing
BHF_park_start has 437163 (82.0%) missing values Missing
BHF_park_ziel has 437163 (82.0%) missing values Missing
BHF_platz has 437162 (82.0%) missing values Missing
BHF_sauberkeit has 437162 (82.0%) missing values Missing
BHF_share has 437162 (82.0%) missing values Missing
BHF_umsteig has 437162 (82.0%) missing values Missing
BHF_velo has 437162 (82.0%) missing values Missing
BHF_velo_non has 437163 (82.0%) missing values Missing
BHF_velo_start has 437163 (82.0%) missing values Missing
BHF_velo_ziel has 437163 (82.0%) missing values Missing
BHF_wc has 437162 (82.0%) missing values Missing
BHF_wegweisung has 437162 (82.0%) missing values Missing
date_of_first_mail has 367358 (68.9%) missing values Missing
date_of_last_access has 165697 (31.1%) missing values Missing
device_type has 328380 (61.6%) missing values Missing
dispcode has 328380 (61.6%) missing values Missing
EKL_fehlende_infos_txt has 205572 (38.6%) missing values Missing
EKL_info_nutzung has 204726 (38.4%) missing values Missing
EKL_info_vermisst has 204726 (38.4%) missing values Missing
EKL_info_zielerreichung has 204726 (38.4%) missing values Missing
EKL_reiseaenderung has 204726 (38.4%) missing values Missing
OES_zuf_personal_angemessen has 95944 (18.0%) missing values Missing
OES_zuf_personal_aufmerksam has 95944 (18.0%) missing values Missing
OES_zuf_personal_effekt has 95944 (18.0%) missing values Missing
Wime_Platz_BhfStart has 437163 (82.0%) missing values Missing
Wime_Umsteig_BhfStart has 437163 (82.0%) missing values Missing
Wime_Sicherheit_BhfStart has 437163 (82.0%) missing values Missing
Wime_WC_BhfStart has 437163 (82.0%) missing values Missing
Wime_Velo_BhfStart has 437163 (82.0%) missing values Missing
Wime_Park_BhfStart has 437163 (82.0%) missing values Missing
Wime_Share_BhfStart has 437163 (82.0%) missing values Missing
Wime_Platz_BhfZiel has 437163 (82.0%) missing values Missing
Wime_Umsteig_BhfZiel has 437163 (82.0%) missing values Missing
Wime_Sicherheit_BhfZiel has 437163 (82.0%) missing values Missing
Wime_WC_BhfZiel has 437163 (82.0%) missing values Missing
Wime_Velo_BhfZiel has 437163 (82.0%) missing values Missing
Wime_Park_BhfZiel has 437163 (82.0%) missing values Missing
Wime_Share_BhfZiel has 437163 (82.0%) missing values Missing
wime_sicherheitskraft has 437163 (82.0%) missing values Missing
fg_abfahrt has 39364 (7.4%) missing values Missing
fg_ankunft has 39366 (7.4%) missing values Missing
ft_abfahrt has 53318 (10.0%) missing values Missing
ft_ankunft has 53318 (10.0%) missing values Missing
ft_haltestellen has 165697 (31.1%) missing values Missing
ft_tu has 165697 (31.1%) missing values Missing
ft_uic_haltestellen has 165697 (31.1%) missing values Missing
OES_beeintraechtigung has 204726 (38.4%) missing values Missing
OES_grund_beeintraecht_2 has 204726 (38.4%) missing values Missing
OES_grund_beeintraecht_5 has 328381 (61.6%) missing values Missing
OES_grund_beeintraecht_6 has 328381 (61.6%) missing values Missing
OES_grund_beeintraecht_7 has 437163 (82.0%) missing values Missing
OES_grund_beeintraecht_7_txt has 437163 (82.0%) missing values Missing
OES_grund_personal_negativ_txt has 95944 (18.0%) missing values Missing
OES_personal_bhf_ziel has 437163 (82.0%) missing values Missing
OES_personal_perron_ziel has 437163 (82.0%) missing values Missing
OES_personal_wunsch has 437163 (82.0%) missing values Missing
Ortskundigkeit has 437163 (82.0%) missing values Missing
R_abo_datum has 367363 (68.9%) missing values Missing
R_abo_nutzung has 367363 (68.9%) missing values Missing
R_abotk_klasse has 367363 (68.9%) missing values Missing
R_anschluss_1 has 367363 (68.9%) missing values Missing
R_anschluss_2 has 367363 (68.9%) missing values Missing
R_anschluss_3 has 367363 (68.9%) missing values Missing
R_grund_nonuse_1 has 328381 (61.6%) missing values Missing
R_grund_nonuse_2 has 328381 (61.6%) missing values Missing
R_grund_nonuse_3 has 328381 (61.6%) missing values Missing
R_grund_nonuse_4 has 328381 (61.6%) missing values Missing
R_grund_nonuse_5 has 328381 (61.6%) missing values Missing
R_grund_nonuse_5txt has 328381 (61.6%) missing values Missing
R_grund_nonuse_6 has 328381 (61.6%) missing values Missing
R_kb_wunsch has 367363 (68.9%) missing values Missing
R_nutzung_einfach has 328380 (61.6%) missing values Missing
R_nutzung_retour has 328380 (61.6%) missing values Missing
R_nutzung_tk has 367363 (68.9%) missing values Missing
R_Park has 437163 (82.0%) missing values Missing
R_platz_andere has 437163 (82.0%) missing values Missing
R_platz_andere_txt has 437163 (82.0%) missing values Missing
R_platz_gebauede has 437163 (82.0%) missing values Missing
R_platz_perron_eng has 437163 (82.0%) missing values Missing
R_platz_perron_leute has 437163 (82.0%) missing values Missing
R_platz_unterf_eng has 437163 (82.0%) missing values Missing
R_platz_unterf_leute has 437163 (82.0%) missing values Missing
R_platz_vorbhf has 437163 (82.0%) missing values Missing
R_platz_warte has 437163 (82.0%) missing values Missing
R_sauber_anderes has 437163 (82.0%) missing values Missing
R_sauber_anderes_txt has 437163 (82.0%) missing values Missing
R_sauber_gebauede has 437163 (82.0%) missing values Missing
R_sauber_perron has 437163 (82.0%) missing values Missing
R_sauber_unterfuehrung has 437163 (82.0%) missing values Missing
R_sauber_vorbhf has 437163 (82.0%) missing values Missing
R_sauber_warte has 437163 (82.0%) missing values Missing
R_sauber_WC has 437163 (82.0%) missing values Missing
R_Sharing has 437163 (82.0%) missing values Missing
R_umsteig_andere has 437163 (82.0%) missing values Missing
R_umsteig_andere_txt has 437163 (82.0%) missing values Missing
R_umsteig_park has 437163 (82.0%) missing values Missing
R_umsteig_trambus has 437163 (82.0%) missing values Missing
R_umsteig_zug has 437163 (82.0%) missing values Missing
R_unzuf_comfort_txt has 11957 (2.2%) missing values Missing
R_unzuf_fahrplan_txt has 367364 (68.9%) missing values Missing
R_unzuf_info_txt has 328381 (61.6%) missing values Missing
R_unzuf_mobile_txt has 367364 (68.9%) missing values Missing
R_unzuf_platzangebot_txt has 328381 (61.6%) missing values Missing
R_unzuf_preis_txt has 367364 (68.9%) missing values Missing
R_unzuf_puenktlichkeit_txt has 328381 (61.6%) missing values Missing
R_unzuf_Sauberkeit_Bhf_txt has 424324 (79.6%) missing values Missing
R_unzuf_sicherheit_zug has 437163 (82.0%) missing values Missing
R_unzuf_stoerungsinfo_txt has 204726 (38.4%) missing values Missing
R_unzuf_Wegweisung_Bhf_txt has 424324 (79.6%) missing values Missing
R_Velo has 437163 (82.0%) missing values Missing
R_WC has 437163 (82.0%) missing values Missing
R_wc_na_start has 437163 (82.0%) missing values Missing
R_wc_na_ziel has 437163 (82.0%) missing values Missing
R_wc_na_zug has 437163 (82.0%) missing values Missing
R_wc_start has 437163 (82.0%) missing values Missing
R_wc_ziel has 437163 (82.0%) missing values Missing
R_wc_zug has 437163 (82.0%) missing values Missing
R_weg_andere has 437163 (82.0%) missing values Missing
R_weg_andere_txt has 437163 (82.0%) missing values Missing
R_weg_laeden has 437163 (82.0%) missing values Missing
R_weg_park has 437163 (82.0%) missing values Missing
R_weg_share has 437163 (82.0%) missing values Missing
R_weg_trambus has 437163 (82.0%) missing values Missing
R_weg_velo has 437163 (82.0%) missing values Missing
R_weg_WC has 437163 (82.0%) missing values Missing
R_weg_zug has 437163 (82.0%) missing values Missing
RF_bhf_abfahrt has 437163 (82.0%) missing values Missing
RF_bhf_andere has 437163 (82.0%) missing values Missing
RF_bhf_perron has 437163 (82.0%) missing values Missing
RF_bhf_touch has 437163 (82.0%) missing values Missing
RF_kanal_1 has 299312 (56.1%) missing values Missing
RF_kanal_12 has 299312 (56.1%) missing values Missing
RF_kanal_13 has 299312 (56.1%) missing values Missing
RF_kanal_14 has 299312 (56.1%) missing values Missing
RF_kanal_15 has 437163 (82.0%) missing values Missing
RF_kanal_2 has 299312 (56.1%) missing values Missing
RF_kanal_4 has 395255 (74.1%) missing values Missing
RF_kanal_6 has 299312 (56.1%) missing values Missing
RF_kanal_7 has 299312 (56.1%) missing values Missing
RF_kanal_8 has 299312 (56.1%) missing values Missing
RF_kanal_keiner has 299312 (56.1%) missing values Missing
RF_kanal_other has 299312 (56.1%) missing values Missing
RF_kanal_other_txt has 299312 (56.1%) missing values Missing
RF_mob_andere has 437163 (82.0%) missing values Missing
RF_mob_andere_txt has 437163 (82.0%) missing values Missing
RF_mob_autom has 437163 (82.0%) missing values Missing
RF_mob_fahrplan has 437163 (82.0%) missing values Missing
RF_mob_karte has 437163 (82.0%) missing values Missing
RF_webs_andere has 437163 (82.0%) missing values Missing
RF_webs_andere_txt has 437163 (82.0%) missing values Missing
RF_webs_erwsuche has 437163 (82.0%) missing values Missing
RF_webs_fahrplan has 437163 (82.0%) missing values Missing
RF_webs_karte has 437163 (82.0%) missing values Missing
RF_Zufallsitem1 has 227748 (42.7%) missing values Missing
RF_zufallsitem1_label has 301740 (56.6%) missing values Missing
S_AB1_GA2kl has 204726 (38.4%) missing values Missing
S_AB2_GA has 328380 (61.6%) missing values Missing
S_AB2_GA1kl has 204726 (38.4%) missing values Missing
S_berufstaetigkeit has 367363 (68.9%) missing values Missing
S_Usertyp1 has 367363 (68.9%) missing values Missing
S_Usertyp2 has 367363 (68.9%) missing values Missing
S_Usertyp3 has 367363 (68.9%) missing values Missing
SF_kanal_1_zuf has 165748 (31.1%) missing values Missing
SF_kanal_12_zuf has 165748 (31.1%) missing values Missing
SF_kanal_13_zuf has 165748 (31.1%) missing values Missing
SF_kanal_14_zuf has 165748 (31.1%) missing values Missing
SF_kanal_2_zuf has 165748 (31.1%) missing values Missing
SF_kanal_4_zuf has 165748 (31.1%) missing values Missing
SF_kanal_6_zuf has 165748 (31.1%) missing values Missing
SF_kanal_7_zuf has 165748 (31.1%) missing values Missing
SF_kanal_8_zuf has 165748 (31.1%) missing values Missing
SF_kanal_other_zuf has 165748 (31.1%) missing values Missing
RF_kanal_1_Zuf has 454847 (85.3%) missing values Missing
RF_kanal_2_Zuf has 524198 (98.3%) missing values Missing
RF_kanal_4_Zuf has 530224 (99.5%) missing values Missing
RF_kanal_6_Zuf has 532423 (99.9%) missing values Missing
RF_kanal_7_Zuf has 528475 (99.1%) missing values Missing
RF_kanal_8_Zuf has 516570 (96.9%) missing values Missing
RF_kanal_12_Zuf has 524394 (98.4%) missing values Missing
RF_kanal_13_Zuf has 523024 (98.1%) missing values Missing
RF_kanal_14_Zuf has 530093 (99.4%) missing values Missing
RF_kanal_24_Zuf has 533106 (100.0%) missing values Missing
SF_bhf_abfahrt has 437163 (82.0%) missing values Missing
SF_bhf_andere has 437163 (82.0%) missing values Missing
SF_bhf_perron has 437163 (82.0%) missing values Missing
SF_bhf_stoerung has 437163 (82.0%) missing values Missing
SF_bhf_touch has 437163 (82.0%) missing values Missing
SF_info_art_4 has 165748 (31.1%) missing values Missing
SF_kanal_15 has 437163 (82.0%) missing values Missing
SF_kanal_4 has 95944 (18.0%) missing values Missing
SF_mob_andere has 437163 (82.0%) missing values Missing
SF_mob_andere_txt has 437163 (82.0%) missing values Missing
SF_mob_autom has 437163 (82.0%) missing values Missing
SF_mob_fahrplan has 437163 (82.0%) missing values Missing
SF_mob_karte has 437163 (82.0%) missing values Missing
u_artikel has 165697 (31.1%) missing values Missing
SF_unzuf_info_txt has 328381 (61.6%) missing values Missing
SF_Zufallsitem1 has 367358 (68.9%) missing values Missing
SF_Zufallsitem2 has 367358 (68.9%) missing values Missing
u_fahrausweis has 367363 (68.9%) missing values Missing
u_ga has 204726 (38.4%) missing values Missing
u_kategorie has 379465 (71.2%) missing values Missing
u_kaufdatum has 49108 (9.2%) missing values Missing
u_zusatz has 54952 (10.3%) missing values Missing
wime_stoerungsinfo has 204726 (38.4%) missing values Missing
wime_unzuf_sf_txt has 367364 (68.9%) missing values Missing
participant has unique values Unique
participant_id has unique values Unique
file_name is an unsupported type, check if it needs cleaning or further analysis Unsupported
RF_kanal_24_Zuf is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-11-18 15:59:54.679459
Analysis finished2022-11-18 16:00:49.524949
Duration54.85 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

time
Date

Distinct2063
Distinct (%)0.4%
Missing96
Missing (%)< 0.1%
Memory size4.1 MiB
Minimum2017-01-01 00:00:00
Maximum2022-11-16 00:00:00
2022-11-18T17:01:02.595927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T17:01:02.870414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

participant
Real number (ℝ≥0)

UNIQUE

Distinct533106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304503.8496
Minimum1
Maximum589965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2022-11-18T17:01:03.061398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26656.25
Q1178082.25
median311358.5
Q3455084.75
95-th percentile563121.75
Maximum589965
Range589964
Interquartile range (IQR)277002.5

Descriptive statistics

Standard deviation172111.5314
Coefficient of variation (CV)0.5652195584
Kurtosis-1.15220962
Mean304503.8496
Median Absolute Deviation (MAD)138501.5
Skewness-0.1258695909
Sum1.623328293 × 1011
Variance2.962237924 × 1010
MonotonicityStrictly increasing
2022-11-18T17:01:03.170591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
4012201
 
< 0.1%
4012181
 
< 0.1%
4012171
 
< 0.1%
4012161
 
< 0.1%
4012151
 
< 0.1%
4012141
 
< 0.1%
4012131
 
< 0.1%
4012121
 
< 0.1%
4012111
 
< 0.1%
Other values (533096)533096
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
ValueCountFrequency (%)
5899651
< 0.1%
5899641
< 0.1%
5899631
< 0.1%
5899621
< 0.1%
5899611
< 0.1%

UmfrageName
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
kuzu_zug
533106 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkuzu_zug
2nd rowkuzu_zug
3rd rowkuzu_zug
4th rowkuzu_zug
5th rowkuzu_zug

Common Values

ValueCountFrequency (%)
kuzu_zug533106
100.0%

Category Frequency Plot

2022-11-18T17:01:03.264570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

file_name
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size4.1 MiB

wime_puenktlich
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
10
201959 
5
112042 
-77
63980 
9
46850 
8
29246 
Other values (7)
79028 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row9
5th row10

Common Values

ValueCountFrequency (%)
10201959
37.9%
5112042
21.0%
-7763980
 
12.0%
946850
 
8.8%
829246
 
5.5%
428561
 
5.4%
112204
 
2.3%
712100
 
2.3%
310090
 
1.9%
66250
 
1.2%
Other values (2)9823
 
1.8%

wime_komfort
Categorical

Distinct12
Distinct (%)< 0.1%
Missing4182
Missing (%)0.8%
Memory size4.1 MiB
10
88437 
5
84838 
8
70001 
-77
62843 
4
58113 
Other values (7)
164692 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row9
5th row10

Common Values

ValueCountFrequency (%)
1088437
16.6%
584838
15.9%
870001
13.1%
-7762843
11.8%
458113
10.9%
953248
10.0%
742572
8.0%
623275
 
4.4%
321359
 
4.0%
18272
 
1.6%
Other values (2)15966
 
3.0%

wime_fahrplan
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
5
140021 
10
118894 
8
59631 
4
52661 
9
48898 
Other values (7)
113000 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row6
3rd row10
4th row4
5th row10

Common Values

ValueCountFrequency (%)
5140021
26.3%
10118894
22.3%
859631
11.2%
452661
 
9.9%
948898
 
9.2%
732886
 
6.2%
322211
 
4.2%
617548
 
3.3%
112479
 
2.3%
210439
 
2.0%
Other values (2)17437
 
3.3%

SF_kanal_zuf
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing367364
Missing (%)68.9%
Memory size4.1 MiB
-77
156751 
5
 
3122
4
 
2582
3
 
1666
2
 
770
Other values (2)
 
851

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77156751
29.4%
53122
 
0.6%
42582
 
0.5%
31666
 
0.3%
2770
 
0.1%
1640
 
0.1%
weiss nicht211
 
< 0.1%
(Missing)367364
68.9%

Category Frequency Plot

2022-11-18T17:01:03.349172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

wime_infokanal
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
239783 
5
101667 
10
69202 
4
35245 
9
26229 
Other values (8)
60979 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
-77239783
45.0%
5101667
19.1%
1069202
 
13.0%
435245
 
6.6%
926229
 
4.9%
823366
 
4.4%
79975
 
1.9%
38778
 
1.6%
weiss nicht7494
 
1.4%
64520
 
0.8%
Other values (3)6846
 
1.3%

wime_personal
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
259300 
10
89346 
weiss nicht
52776 
5
48790 
9
 
25707
Other values (7)
57186 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row10
4th row10
5th row-77

Common Values

ValueCountFrequency (%)
-77259300
48.6%
1089346
 
16.8%
weiss nicht52776
 
9.9%
548790
 
9.2%
925707
 
4.8%
823164
 
4.3%
414199
 
2.7%
78937
 
1.7%
63870
 
0.7%
33616
 
0.7%
Other values (2)3400
 
0.6%

wime_kundenorientierung
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing367364
Missing (%)68.9%
Memory size4.1 MiB
-77
67560 
5
40400 
4
36900 
3
10434 
weiss nicht
 
6360
Other values (2)
 
4088

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row5
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7767560
 
12.7%
540400
 
7.6%
436900
 
6.9%
310434
 
2.0%
weiss nicht6360
 
1.2%
22734
 
0.5%
11354
 
0.3%
(Missing)367364
68.9%

Category Frequency Plot

2022-11-18T17:01:03.451641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

wime_mobile
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing367364
Missing (%)68.9%
Memory size4.1 MiB
-77
48867 
5
39969 
4
28854 
weiss nicht
28742 
3
11762 
Other values (2)
7548 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row5
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7748867
 
9.2%
539969
 
7.5%
428854
 
5.4%
weiss nicht28742
 
5.4%
311762
 
2.2%
24806
 
0.9%
12742
 
0.5%
(Missing)367364
68.9%

Category Frequency Plot

2022-11-18T17:01:03.564010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

wime_platzangebot
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
10
134433 
5
99435 
-77
63980 
8
49803 
4
44799 
Other values (7)
140655 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row6
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10134433
25.2%
599435
18.7%
-7763980
12.0%
849803
 
9.3%
444799
 
8.4%
942398
 
8.0%
728634
 
5.4%
322755
 
4.3%
616576
 
3.1%
115904
 
3.0%
Other values (2)14388
 
2.7%

wime_preis_leistung
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
5
105563 
4
74675 
10
73110 
8
52899 
3
48042 
Other values (7)
178816 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row4
3rd row10
4th row7
5th row10

Common Values

ValueCountFrequency (%)
5105563
19.8%
474675
14.0%
1073110
13.7%
852899
9.9%
348042
9.0%
743074
8.1%
-7738312
 
7.2%
628542
 
5.4%
928345
 
5.3%
219113
 
3.6%
Other values (2)21430
 
4.0%

Wime_Sauberkeit_BhfZiel
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
4
63974 
5
58900 
-77
49782 
3
19170 
weiss nicht
8847 
Other values (2)
 
4052

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row5
4th rowweiss nicht
5th row5

Common Values

ValueCountFrequency (%)
463974
 
12.0%
558900
 
11.0%
-7749782
 
9.3%
319170
 
3.6%
weiss nicht8847
 
1.7%
23187
 
0.6%
1865
 
0.2%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:03.682641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Sauberkeit_BhfStart
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
4
64608 
5
58960 
-77
49782 
3
19326 
weiss nicht
7689 
Other values (2)
 
4360

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
464608
 
12.1%
558960
 
11.1%
-7749782
 
9.3%
319326
 
3.6%
weiss nicht7689
 
1.4%
23361
 
0.6%
1999
 
0.2%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:03.801598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

wime_sauberkeit
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
10
91319 
5
83332 
8
72496 
-77
63980 
4
61802 
Other values (7)
160176 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row9
5th row5

Common Values

ValueCountFrequency (%)
1091319
17.1%
583332
15.6%
872496
13.6%
-7763980
12.0%
461802
11.6%
956524
10.6%
742740
8.0%
322913
 
4.3%
621813
 
4.1%
27160
 
1.3%
Other values (2)9026
 
1.7%

wime_wc
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
471251 
5
 
10510
4
 
9861
8
 
7762
10
 
7676
Other values (7)
 
26045

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77471251
88.4%
510510
 
2.0%
49861
 
1.8%
87762
 
1.5%
107676
 
1.4%
75709
 
1.1%
35652
 
1.1%
95172
 
1.0%
63825
 
0.7%
22477
 
0.5%
Other values (2)3210
 
0.6%

wime_oes_ziel
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
10
135140 
5
83384 
-77
75969 
9
67427 
8
60114 
Other values (8)
111071 

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10135140
25.3%
583384
15.6%
-7775969
14.3%
967427
12.6%
860114
11.3%
444621
 
8.4%
728952
 
5.4%
613569
 
2.5%
313502
 
2.5%
weiss nicht4676
 
0.9%
Other values (3)5751
 
1.1%

wime_oes_start
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
10
150932 
5
89601 
-77
78214 
9
63632 
8
55703 
Other values (7)
95023 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10150932
28.3%
589601
16.8%
-7778214
14.7%
963632
11.9%
855703
 
10.4%
440063
 
7.5%
724889
 
4.7%
310743
 
2.0%
610525
 
2.0%
weiss nicht3842
 
0.7%
Other values (2)4961
 
0.9%

wime_oes_fahrt
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
10
168056 
5
95825 
-77
78214 
9
70396 
8
51206 
Other values (8)
69408 

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10168056
31.5%
595825
18.0%
-7778214
14.7%
970396
13.2%
851206
 
9.6%
434607
 
6.5%
717387
 
3.3%
66313
 
1.2%
36053
 
1.1%
weiss nicht1899
 
0.4%
Other values (3)3149
 
0.6%

wime_sf_behebung
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing367364
Missing (%)68.9%
Memory size4.1 MiB
-77
155779 
4
 
2446
5
 
2443
3
 
1937
1
 
1171
Other values (2)
 
1966

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77155779
29.2%
42446
 
0.5%
52443
 
0.5%
31937
 
0.4%
11171
 
0.2%
weiss nicht1006
 
0.2%
2960
 
0.2%
(Missing)367364
68.9%

Category Frequency Plot

2022-11-18T17:01:03.911501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

wime_wc_verfuegb
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
461996 
10
 
13494
5
 
13037
4
 
8579
8
 
7556
Other values (7)
 
28443

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rowweiss nicht
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77461996
86.7%
1013494
 
2.5%
513037
 
2.4%
48579
 
1.6%
87556
 
1.4%
96412
 
1.2%
15484
 
1.0%
74464
 
0.8%
34236
 
0.8%
62737
 
0.5%
Other values (2)5110
 
1.0%

wime_gastro_ambiente
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
504398 
weiss nicht
 
5105
8
 
4381
10
 
3839
5
 
3494
Other values (8)
 
11888

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77504398
94.6%
weiss nicht5105
 
1.0%
84381
 
0.8%
103839
 
0.7%
53494
 
0.7%
73022
 
0.6%
92710
 
0.5%
42646
 
0.5%
61799
 
0.3%
31046
 
0.2%
Other values (3)665
 
0.1%

wime_gastro_auswahl
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
504398 
weiss nicht
 
6521
8
 
3691
10
 
3680
5
 
3187
Other values (8)
 
11628

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77504398
94.6%
weiss nicht6521
 
1.2%
83691
 
0.7%
103680
 
0.7%
53187
 
0.6%
72782
 
0.5%
42563
 
0.5%
92313
 
0.4%
61723
 
0.3%
31267
 
0.2%
Other values (3)980
 
0.2%

wime_gastro_freundlich
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
504398 
10
 
7844
weiss nicht
 
5612
5
 
3707
9
 
3443
Other values (8)
 
8101

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77504398
94.6%
107844
 
1.5%
weiss nicht5612
 
1.1%
53707
 
0.7%
93443
 
0.6%
83386
 
0.6%
71588
 
0.3%
41358
 
0.3%
6812
 
0.2%
3452
 
0.1%
Other values (3)505
 
0.1%

wime_gastro_kompetenz
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
504398 
weiss nicht
 
6839
10
 
6386
8
 
3529
5
 
3333
Other values (8)
 
8620

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77504398
94.6%
weiss nicht6839
 
1.3%
106386
 
1.2%
83529
 
0.7%
53333
 
0.6%
93102
 
0.6%
71843
 
0.3%
41666
 
0.3%
6928
 
0.2%
3541
 
0.1%
Other values (3)540
 
0.1%

wime_gastro_preis
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
504398 
weiss nicht
 
5416
5
 
3490
8
 
3407
4
 
3328
Other values (8)
 
13066

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77504398
94.6%
weiss nicht5416
 
1.0%
53490
 
0.7%
83407
 
0.6%
43328
 
0.6%
72975
 
0.6%
102362
 
0.4%
62347
 
0.4%
31975
 
0.4%
91823
 
0.3%
Other values (3)1584
 
0.3%

wime_gastro_qualitaet
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
504398 
weiss nicht
 
5757
10
 
4544
8
 
4143
5
 
3305
Other values (8)
 
10958

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77504398
94.6%
weiss nicht5757
 
1.1%
104544
 
0.9%
84143
 
0.8%
53305
 
0.6%
92919
 
0.5%
72614
 
0.5%
42412
 
0.5%
61480
 
0.3%
3874
 
0.2%
Other values (3)659
 
0.1%

Wime_Wegweisung_BhfZiel
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
5
76548 
4
53565 
-77
49782 
3
13393 
weiss nicht
7656 
Other values (2)
 
3781

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
576548
 
14.4%
453565
 
10.0%
-7749782
 
9.3%
313393
 
2.5%
weiss nicht7656
 
1.4%
22787
 
0.5%
1994
 
0.2%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:04.012575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Wegweisung_BhfStart
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
5
81189 
4
52153 
-77
49782 
3
11782 
weiss nicht
 
6486
Other values (2)
 
3333

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
581189
 
15.2%
452153
 
9.8%
-7749782
 
9.3%
311782
 
2.2%
weiss nicht6486
 
1.2%
22440
 
0.5%
1893
 
0.2%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:04.107154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

BFH_sahre_non
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
quoted
55846 
-77
39720 
not quoted
 
377

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted55846
 
10.5%
-7739720
 
7.5%
not quoted377
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:04.183067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

BFH_sahre_start
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
48834 
not quoted
46892 
quoted
 
217

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownot quoted
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7748834
 
9.2%
not quoted46892
 
8.8%
quoted217
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:04.311246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

BFH_sahre_ziel
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
59080 
not quoted
36683 
quoted
 
180

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-7759080
 
11.1%
not quoted36683
 
6.9%
quoted180
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:04.378751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

BHF_park
Categorical

HIGH CARDINALITY
MISSING

Distinct127
Distinct (%)0.1%
Missing437162
Missing (%)82.0%
Memory size4.1 MiB
-66
95643 
Sion
 
17
Olten
 
15
Bern
 
11
Täsch
 
9
Other values (122)
 
249

Unique

Unique76 ?
Unique (%)0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695643
 
17.9%
Sion17
 
< 0.1%
Olten15
 
< 0.1%
Bern11
 
< 0.1%
Täsch9
 
< 0.1%
Coppet8
 
< 0.1%
Liestal8
 
< 0.1%
Neuchâtel7
 
< 0.1%
Lugano7
 
< 0.1%
Zürich HB7
 
< 0.1%
Other values (117)212
 
< 0.1%
(Missing)437162
82.0%

BHF_park_non
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
quoted
52524 
-77
39720 
not quoted
 
3699

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted52524
 
9.9%
-7739720
 
7.5%
not quoted3699
 
0.7%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:04.447313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

BHF_park_start
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
48834 
not quoted
44428 
quoted
 
2681

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownot quoted
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7748834
 
9.2%
not quoted44428
 
8.3%
quoted2681
 
0.5%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:04.516589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

BHF_park_ziel
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
59080 
not quoted
35829 
quoted
 
1034

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-7759080
 
11.1%
not quoted35829
 
6.7%
quoted1034
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:04.582765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

BHF_platz
Categorical

HIGH CARDINALITY
MISSING

Distinct399
Distinct (%)0.4%
Missing437162
Missing (%)82.0%
Memory size4.1 MiB
-66
92359 
Bern
 
595
Basel SBB
 
275
Lausanne
 
246
Zürich HB
 
201
Other values (394)
 
2268

Unique

Unique217 ?
Unique (%)0.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6692359
 
17.3%
Bern595
 
0.1%
Basel SBB275
 
0.1%
Lausanne246
 
< 0.1%
Zürich HB201
 
< 0.1%
Luzern186
 
< 0.1%
Olten131
 
< 0.1%
Genève104
 
< 0.1%
Liestal77
 
< 0.1%
Biel/Bienne65
 
< 0.1%
Other values (389)1705
 
0.3%
(Missing)437162
82.0%

BHF_sauberkeit
Categorical

HIGH CARDINALITY
MISSING

Distinct481
Distinct (%)0.5%
Missing437162
Missing (%)82.0%
Memory size4.1 MiB
-66
93233 
Zürich HB
 
221
Bern
 
208
Lausanne
 
164
Genève
 
91
Other values (476)
 
2027

Unique

Unique260 ?
Unique (%)0.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th rowRheinfelden

Common Values

ValueCountFrequency (%)
-6693233
 
17.5%
Zürich HB221
 
< 0.1%
Bern208
 
< 0.1%
Lausanne164
 
< 0.1%
Genève91
 
< 0.1%
Biel/Bienne88
 
< 0.1%
Olten82
 
< 0.1%
Basel SBB76
 
< 0.1%
Milano Centrale67
 
< 0.1%
Luzern46
 
< 0.1%
Other values (471)1668
 
0.3%
(Missing)437162
82.0%

BHF_share
Categorical

MISSING

Distinct11
Distinct (%)< 0.1%
Missing437162
Missing (%)82.0%
Memory size4.1 MiB
-66
95932 
Neuchâtel
 
2
Zürich HB
 
2
Dachsen
 
1
Fribourg/Freiburg
 
1
Other values (6)
 
6

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695932
 
18.0%
Neuchâtel2
 
< 0.1%
Zürich HB2
 
< 0.1%
Dachsen1
 
< 0.1%
Fribourg/Freiburg1
 
< 0.1%
Bellinzona1
 
< 0.1%
Bern1
 
< 0.1%
Interlaken Ost1
 
< 0.1%
Ardon1
 
< 0.1%
Genève1
 
< 0.1%
(Missing)437162
82.0%

BHF_umsteig
Categorical

HIGH CARDINALITY
MISSING

Distinct531
Distinct (%)0.6%
Missing437162
Missing (%)82.0%
Memory size4.1 MiB
-66
92294 
Bern
 
457
Zürich HB
 
340
Basel SBB
 
193
Luzern
 
158
Other values (526)
 
2502

Unique

Unique297 ?
Unique (%)0.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6692294
 
17.3%
Bern457
 
0.1%
Zürich HB340
 
0.1%
Basel SBB193
 
< 0.1%
Luzern158
 
< 0.1%
Olten142
 
< 0.1%
Lausanne100
 
< 0.1%
Liestal65
 
< 0.1%
Visp64
 
< 0.1%
Winterthur62
 
< 0.1%
Other values (521)2069
 
0.4%
(Missing)437162
82.0%

BHF_velo
Categorical

HIGH CARDINALITY
MISSING

Distinct149
Distinct (%)0.2%
Missing437162
Missing (%)82.0%
Memory size4.1 MiB
-66
95272 
Zürich HB
 
67
Bern
 
63
Basel SBB
 
57
Luzern
 
45
Other values (144)
 
440

Unique

Unique81 ?
Unique (%)0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695272
 
17.9%
Zürich HB67
 
< 0.1%
Bern63
 
< 0.1%
Basel SBB57
 
< 0.1%
Luzern45
 
< 0.1%
Genève25
 
< 0.1%
Aarau22
 
< 0.1%
Lausanne20
 
< 0.1%
Winterthur18
 
< 0.1%
Solothurn17
 
< 0.1%
Other values (139)338
 
0.1%
(Missing)437162
82.0%

BHF_velo_non
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
quoted
52822 
-77
39720 
not quoted
 
3401

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted52822
 
9.9%
-7739720
 
7.5%
not quoted3401
 
0.6%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:04.654646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

BHF_velo_start
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
48834 
not quoted
44595 
quoted
 
2514

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownot quoted
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7748834
 
9.2%
not quoted44595
 
8.4%
quoted2514
 
0.5%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:04.721640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

BHF_velo_ziel
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
59080 
not quoted
35879 
quoted
 
984

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-7759080
 
11.1%
not quoted35879
 
6.7%
quoted984
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:04.788018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

BHF_wc
Categorical

HIGH CARDINALITY
MISSING

Distinct65
Distinct (%)0.1%
Missing437162
Missing (%)82.0%
Memory size4.1 MiB
-66
95819 
Zürich HB
 
12
Luzern
 
8
Genève
 
6
Olten
 
5
Other values (60)
 
94

Unique

Unique42 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695819
 
18.0%
Zürich HB12
 
< 0.1%
Luzern8
 
< 0.1%
Genève6
 
< 0.1%
Olten5
 
< 0.1%
Chur4
 
< 0.1%
Brig4
 
< 0.1%
Bern4
 
< 0.1%
Genève-Aéroport4
 
< 0.1%
St. Gallen4
 
< 0.1%
Other values (55)74
 
< 0.1%
(Missing)437162
82.0%

BHF_wegweisung
Categorical

HIGH CARDINALITY
MISSING

Distinct420
Distinct (%)0.4%
Missing437162
Missing (%)82.0%
Memory size4.1 MiB
-66
93683 
Zürich HB
 
260
Bern
 
159
Lausanne
 
135
Olten
 
81
Other values (415)
 
1626

Unique

Unique234 ?
Unique (%)0.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6693683
 
17.6%
Zürich HB260
 
< 0.1%
Bern159
 
< 0.1%
Lausanne135
 
< 0.1%
Olten81
 
< 0.1%
Genève66
 
< 0.1%
Basel SBB63
 
< 0.1%
Liestal56
 
< 0.1%
Luzern45
 
< 0.1%
Genève-Aéroport45
 
< 0.1%
Other values (410)1351
 
0.3%
(Missing)437162
82.0%

createdAt
Categorical

HIGH CARDINALITY

Distinct447792
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2019-10-09 15:37:25.620
 
4
2019-10-09 16:45:05.973
 
4
2019-10-09 15:59:46.923
 
4
2019-10-09 16:02:01.723
 
4
2019-10-09 15:28:20.710
 
4
Other values (447787)
533086 

Unique

Unique381783 ?
Unique (%)71.6%

Sample

1st row2019-07-19 22:15:07.430
2nd row2019-07-19 22:15:07.803
3rd row2019-07-19 22:15:07.920
4th row2019-07-19 22:15:08.033
5th row2019-07-19 22:15:08.143

Common Values

ValueCountFrequency (%)
2019-10-09 15:37:25.6204
 
< 0.1%
2019-10-09 16:45:05.9734
 
< 0.1%
2019-10-09 15:59:46.9234
 
< 0.1%
2019-10-09 16:02:01.7234
 
< 0.1%
2019-10-09 15:28:20.7104
 
< 0.1%
2019-10-09 16:53:09.2174
 
< 0.1%
2019-10-09 16:05:21.8804
 
< 0.1%
2019-10-09 16:16:32.8934
 
< 0.1%
2019-10-09 16:32:37.1804
 
< 0.1%
2019-10-09 15:36:49.5574
 
< 0.1%
Other values (447782)533066
> 99.9%

date_of_first_mail
Categorical

HIGH CARDINALITY
MISSING

Distinct43568
Distinct (%)26.3%
Missing367358
Missing (%)68.9%
Memory size4.1 MiB
2021-06-28 18:04:16
 
23
2022-07-15 14:40:36
 
20
2022-06-22 17:20:09
 
20
2022-11-02 19:30:22
 
19
2022-09-28 19:01:44
 
19
Other values (43563)
165647 

Unique

Unique10917 ?
Unique (%)6.6%

Sample

1st row2020-11-11 16:30:22
2nd row2020-11-11 16:30:23
3rd row2020-11-11 16:30:23
4th row2020-11-11 16:30:24
5th row2020-11-11 16:30:24

Common Values

ValueCountFrequency (%)
2021-06-28 18:04:1623
 
< 0.1%
2022-07-15 14:40:3620
 
< 0.1%
2022-06-22 17:20:0920
 
< 0.1%
2022-11-02 19:30:2219
 
< 0.1%
2022-09-28 19:01:4419
 
< 0.1%
2022-08-17 15:00:1818
 
< 0.1%
2022-08-15 09:31:0118
 
< 0.1%
2021-09-29 17:40:5718
 
< 0.1%
2022-08-17 14:50:1918
 
< 0.1%
2022-08-29 13:11:2318
 
< 0.1%
Other values (43558)165557
31.1%
(Missing)367358
68.9%

date_of_last_access
Categorical

HIGH CARDINALITY
MISSING

Distinct364966
Distinct (%)99.3%
Missing165697
Missing (%)31.1%
Memory size4.1 MiB
2022-09-26 16:09:28
 
3
2019-04-25 14:34:01
 
3
2020-08-10 20:28:47
 
3
2018-09-26 16:23:29
 
3
2019-03-25 19:34:43
 
3
Other values (364961)
367394 

Unique

Unique362544 ?
Unique (%)98.7%

Sample

1st row2018-07-04 19:10:49
2nd row2018-07-04 07:31:05
3rd row2018-07-04 14:38:25
4th row2018-07-04 08:05:31
5th row2018-07-04 08:41:28

Common Values

ValueCountFrequency (%)
2022-09-26 16:09:283
 
< 0.1%
2019-04-25 14:34:013
 
< 0.1%
2020-08-10 20:28:473
 
< 0.1%
2018-09-26 16:23:293
 
< 0.1%
2019-03-25 19:34:433
 
< 0.1%
2022-08-08 12:08:133
 
< 0.1%
2022-07-19 12:42:143
 
< 0.1%
2020-06-09 15:42:293
 
< 0.1%
2019-05-06 15:46:433
 
< 0.1%
2022-06-28 13:18:443
 
< 0.1%
Other values (364956)367379
68.9%
(Missing)165697
31.1%

device_type
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing328380
Missing (%)61.6%
Memory size4.1 MiB
Desktop
137493 
Smartphone
67233 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesktop
2nd rowDesktop
3rd rowDesktop
4th rowDesktop
5th rowDesktop

Common Values

ValueCountFrequency (%)
Desktop137493
25.8%
Smartphone67233
 
12.6%
(Missing)328380
61.6%

Category Frequency Plot

2022-11-18T17:01:04.876100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

dispcode
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing328380
Missing (%)61.6%
Memory size4.1 MiB
Beendet
156072 
Ausgescreent
44414 
Beendet nach Unterbrechung
 
4240

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBeendet
2nd rowBeendet
3rd rowBeendet
4th rowBeendet
5th rowBeendet

Common Values

ValueCountFrequency (%)
Beendet156072
29.3%
Ausgescreent44414
 
8.3%
Beendet nach Unterbrechung4240
 
0.8%
(Missing)328380
61.6%

Category Frequency Plot

2022-11-18T17:01:04.952535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

EKL_fehlende_infos_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct693
Distinct (%)0.2%
Missing205572
Missing (%)38.6%
Memory size4.1 MiB
-66
326711 
-99
 
114
App
 
11
SBB App
 
4
Smartphone
 
3
Other values (688)
 
691

Unique

Unique685 ?
Unique (%)0.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66326711
61.3%
-99114
 
< 0.1%
App11
 
< 0.1%
SBB App4
 
< 0.1%
Smartphone3
 
< 0.1%
SMS2
 
< 0.1%
mobile2
 
< 0.1%
Durchsage im Zug2
 
< 0.1%
konkret, wie ich alternativ weiterreisen kann, auf meinem Smartphone1
 
< 0.1%
schnellster Weg nach Thun ab Wankdorf (zurück nach Bern?, warten?)1
 
< 0.1%
Other values (683)683
 
0.1%
(Missing)205572
38.6%

EKL_info_nutzung
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing204726
Missing (%)38.4%
Memory size4.1 MiB
-77
324728 
ja, ich habe den vorgeschlagenen Reiseweg gewählt
 
2338
ja, ich bin später gereist
 
784
ja, ich habe einen anderen als den vorgeschlagenen Reiseweg gewählt
 
300
nein
 
159
Other values (2)
 
71

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77324728
60.9%
ja, ich habe den vorgeschlagenen Reiseweg gewählt2338
 
0.4%
ja, ich bin später gereist784
 
0.1%
ja, ich habe einen anderen als den vorgeschlagenen Reiseweg gewählt300
 
0.1%
nein159
 
< 0.1%
ja, ich habe meine Reise abgebrochen / nicht angetreten40
 
< 0.1%
weiss nicht31
 
< 0.1%
(Missing)204726
38.4%

Category Frequency Plot

2022-11-18T17:01:05.039891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

EKL_info_vermisst
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing204726
Missing (%)38.4%
Memory size4.1 MiB
-77
324257 
Ja
 
2132
Nein
 
1733
Weiss nicht
 
258

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77324257
60.8%
Ja2132
 
0.4%
Nein1733
 
0.3%
Weiss nicht258
 
< 0.1%
(Missing)204726
38.4%

Category Frequency Plot

2022-11-18T17:01:05.125602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

EKL_info_zielerreichung
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing204726
Missing (%)38.4%
Memory size4.1 MiB
-77
320123 
Nein
 
4150
Ja
 
3723
Weiss nicht
 
384

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77320123
60.0%
Nein4150
 
0.8%
Ja3723
 
0.7%
Weiss nicht384
 
0.1%
(Missing)204726
38.4%

Category Frequency Plot

2022-11-18T17:01:05.200580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

EKL_reiseaenderung
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing204726
Missing (%)38.4%
Memory size4.1 MiB
-77
302975 
Nein
 
16899
Ja
 
8248
Weiss nicht
 
258

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77302975
56.8%
Nein16899
 
3.2%
Ja8248
 
1.5%
Weiss nicht258
 
< 0.1%
(Missing)204726
38.4%

Category Frequency Plot

2022-11-18T17:01:05.274116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_allgemein
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
10
88998 
8
87621 
9
77460 
-77
75967 
5
66200 
Other values (7)
136860 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row9
3rd row10
4th row8
5th row10

Common Values

ValueCountFrequency (%)
1088998
16.7%
887621
16.4%
977460
14.5%
-7775967
14.2%
566200
12.4%
460726
11.4%
737476
7.0%
315494
 
2.9%
613062
 
2.5%
weiss nicht5238
 
1.0%
Other values (2)4864
 
0.9%

OES_zuf_personal_angemessen
Categorical

MISSING

Distinct13
Distinct (%)< 0.1%
Missing95944
Missing (%)18.0%
Memory size4.1 MiB
-77
359850 
10
 
23266
99
 
19703
9
 
9974
8
 
8758
Other values (8)
 
15611

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77359850
67.5%
1023266
 
4.4%
9919703
 
3.7%
99974
 
1.9%
88758
 
1.6%
57489
 
1.4%
43001
 
0.6%
72880
 
0.5%
61167
 
0.2%
3651
 
0.1%
Other values (3)423
 
0.1%
(Missing)95944
 
18.0%

OES_zuf_personal_aufmerksam
Categorical

MISSING

Distinct13
Distinct (%)< 0.1%
Missing95944
Missing (%)18.0%
Memory size4.1 MiB
-77
359850 
weiss nicht
 
23792
10
 
12255
8
 
12037
9
 
8585
Other values (8)
 
20643

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77359850
67.5%
weiss nicht23792
 
4.5%
1012255
 
2.3%
812037
 
2.3%
98585
 
1.6%
55874
 
1.1%
75651
 
1.1%
44488
 
0.8%
62482
 
0.5%
31448
 
0.3%
Other values (3)700
 
0.1%
(Missing)95944
 
18.0%

OES_zuf_personal_effekt
Categorical

MISSING

Distinct13
Distinct (%)< 0.1%
Missing95944
Missing (%)18.0%
Memory size4.1 MiB
-77
359850 
10
 
17564
8
 
11551
weiss nicht
 
10658
5
 
10620
Other values (8)
 
26919

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77359850
67.5%
1017564
 
3.3%
811551
 
2.2%
weiss nicht10658
 
2.0%
510620
 
2.0%
98510
 
1.6%
75997
 
1.1%
44652
 
0.9%
64318
 
0.8%
32652
 
0.5%
Other values (3)790
 
0.1%
(Missing)95944
 
18.0%

wime_gesamtzuf
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
10
109049 
5
94130 
9
83651 
8
68393 
4
57244 
Other values (8)
120638 

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10
2nd row8
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10109049
20.5%
594130
17.7%
983651
15.7%
868393
12.8%
457244
10.7%
-7753789
10.1%
728971
 
5.4%
314111
 
2.6%
611899
 
2.2%
15368
 
1.0%
Other values (3)6500
 
1.2%

Wime_Platz_BhfStart
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
5
29976 
-77
28357 
4
25536 
3
7926 
2
 
1847
Other values (2)
 
2301

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row-77
4th row4
5th row4

Common Values

ValueCountFrequency (%)
529976
 
5.6%
-7728357
 
5.3%
425536
 
4.8%
37926
 
1.5%
21847
 
0.3%
weiss nicht1805
 
0.3%
1496
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:05.358131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Umsteig_BhfStart
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
28357 
5
27614 
4
23528 
weiss nicht
7131 
3
6910 
Other values (2)
 
2403

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row-77
4th row4
5th row4

Common Values

ValueCountFrequency (%)
-7728357
 
5.3%
527614
 
5.2%
423528
 
4.4%
weiss nicht7131
 
1.3%
36910
 
1.3%
21690
 
0.3%
1713
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:05.442049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Sicherheit_BhfStart
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
90229 
5
 
2120
4
 
1807
weiss nicht
 
1299
3
 
401
Other values (2)
 
87

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7790229
 
16.9%
52120
 
0.4%
41807
 
0.3%
weiss nicht1299
 
0.2%
3401
 
0.1%
256
 
< 0.1%
131
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:05.524136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_WC_BhfStart
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
95337 
4
 
212
5
 
179
3
 
103
2
 
48
Other values (2)
 
64

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7795337
 
17.9%
4212
 
< 0.1%
5179
 
< 0.1%
3103
 
< 0.1%
248
 
< 0.1%
134
 
< 0.1%
weiss nicht30
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:05.602742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Velo_BhfStart
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93429 
4
 
687
5
 
611
3
 
554
2
 
339
Other values (2)
 
323

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7793429
 
17.5%
4687
 
0.1%
5611
 
0.1%
3554
 
0.1%
2339
 
0.1%
1188
 
< 0.1%
weiss nicht135
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:05.681832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Park_BhfStart
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93263 
5
 
1217
4
 
784
3
 
300
2
 
149
Other values (2)
 
230

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7793263
 
17.5%
51217
 
0.2%
4784
 
0.1%
3300
 
0.1%
2149
 
< 0.1%
weiss nicht123
 
< 0.1%
1107
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:05.846316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Share_BhfStart
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
95726 
weiss nicht
 
72
5
 
63
4
 
48
3
 
21
Other values (2)
 
13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7795726
 
18.0%
weiss nicht72
 
< 0.1%
563
 
< 0.1%
448
 
< 0.1%
321
 
< 0.1%
28
 
< 0.1%
15
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:05.927466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Platz_BhfZiel
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
5
28481 
-77
28357 
4
25786 
3
8669 
weiss nicht
 
2201
Other values (2)
 
2449

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row-77
4th row4
5th row4

Common Values

ValueCountFrequency (%)
528481
 
5.3%
-7728357
 
5.3%
425786
 
4.8%
38669
 
1.6%
weiss nicht2201
 
0.4%
21956
 
0.4%
1493
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:06.009569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Umsteig_BhfZiel
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
28357 
5
27417 
4
23037 
3
7359 
weiss nicht
6862 
Other values (2)
2911 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row-77
4th row4
5th row3

Common Values

ValueCountFrequency (%)
-7728357
 
5.3%
527417
 
5.1%
423037
 
4.3%
37359
 
1.4%
weiss nicht6862
 
1.3%
22095
 
0.4%
1816
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:06.093959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Sicherheit_BhfZiel
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
91239 
5
 
1841
4
 
1525
weiss nicht
 
909
3
 
349
Other values (2)
 
80

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7791239
 
17.1%
51841
 
0.3%
41525
 
0.3%
weiss nicht909
 
0.2%
3349
 
0.1%
252
 
< 0.1%
128
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:06.175606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_WC_BhfZiel
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
95367 
4
 
194
5
 
194
3
 
97
2
 
39
Other values (2)
 
52

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7795367
 
17.9%
4194
 
< 0.1%
5194
 
< 0.1%
397
 
< 0.1%
239
 
< 0.1%
126
 
< 0.1%
weiss nicht26
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:06.253395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Velo_BhfZiel
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
94959 
5
 
253
4
 
246
3
 
191
2
 
139
Other values (2)
 
155

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7794959
 
17.8%
5253
 
< 0.1%
4246
 
< 0.1%
3191
 
< 0.1%
2139
 
< 0.1%
179
 
< 0.1%
weiss nicht76
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:06.331413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Park_BhfZiel
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
94909 
5
 
478
4
 
296
3
 
115
weiss nicht
 
73
Other values (2)
 
72

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7794909
 
17.8%
5478
 
0.1%
4296
 
0.1%
3115
 
< 0.1%
weiss nicht73
 
< 0.1%
242
 
< 0.1%
130
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:06.410581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Wime_Share_BhfZiel
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
95763 
5
 
60
4
 
56
weiss nicht
 
36
3
 
20
Other values (2)
 
8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7795763
 
18.0%
560
 
< 0.1%
456
 
< 0.1%
weiss nicht36
 
< 0.1%
320
 
< 0.1%
16
 
< 0.1%
22
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:06.489376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

wime_sicherheitskraft
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
94061 
5
 
870
weiss nicht
 
476
4
 
354
3
 
131
Other values (2)
 
51

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7794061
 
17.6%
5870
 
0.2%
weiss nicht476
 
0.1%
4354
 
0.1%
3131
 
< 0.1%
131
 
< 0.1%
220
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:06.570483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

fg_abfahrt
Categorical

HIGH CARDINALITY
MISSING

Distinct1399
Distinct (%)0.3%
Missing39364
Missing (%)7.4%
Memory size4.1 MiB
17:02:00
 
1619
17:04:00
 
1542
17:00:00
 
1456
07:32:00
 
1447
16:02:00
 
1439
Other values (1394)
486239 

Unique

Unique41 ?
Unique (%)< 0.1%

Sample

1st row09:52:00
2nd row17:04:00
3rd row16:42:00
4th row13:06:00
5th row17:43:00

Common Values

ValueCountFrequency (%)
17:02:001619
 
0.3%
17:04:001542
 
0.3%
17:00:001456
 
0.3%
07:32:001447
 
0.3%
16:02:001439
 
0.3%
17:34:001379
 
0.3%
16:32:001352
 
0.3%
17:32:001345
 
0.3%
07:34:001288
 
0.2%
16:34:001255
 
0.2%
Other values (1389)479620
90.0%
(Missing)39364
 
7.4%

fg_ankunft
Categorical

HIGH CARDINALITY
MISSING

Distinct1415
Distinct (%)0.3%
Missing39366
Missing (%)7.4%
Memory size4.1 MiB
17:56:00
 
1534
18:28:00
 
1436
18:00:00
 
1312
17:58:00
 
1286
17:28:00
 
1257
Other values (1410)
486915 

Unique

Unique38 ?
Unique (%)< 0.1%

Sample

1st row10:39:00
2nd row17:50:00
3rd row18:45:00
4th row13:57:00
5th row17:55:00

Common Values

ValueCountFrequency (%)
17:56:001534
 
0.3%
18:28:001436
 
0.3%
18:00:001312
 
0.2%
17:58:001286
 
0.2%
17:28:001257
 
0.2%
18:56:001252
 
0.2%
08:56:001204
 
0.2%
18:25:001195
 
0.2%
08:24:001168
 
0.2%
16:56:001165
 
0.2%
Other values (1405)480931
90.2%
(Missing)39366
 
7.4%

fg_startort
Categorical

HIGH CARDINALITY

Distinct15985
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Zürich HB
 
34065
Bern
 
22706
Basel SBB
 
18498
Zürich Flughafen
 
16839
Luzern
 
13864
Other values (15980)
427134 

Unique

Unique4441 ?
Unique (%)0.8%

Sample

1st rowUrdorf, Neumatt
2nd rowRigi Kulm
3rd rowPorrentruy
4th rowBern
5th rowDiessenhofen

Common Values

ValueCountFrequency (%)
Zürich HB34065
 
6.4%
Bern22706
 
4.3%
Basel SBB18498
 
3.5%
Zürich Flughafen16839
 
3.2%
Luzern13864
 
2.6%
Genève12976
 
2.4%
Lausanne12007
 
2.3%
-669496
 
1.8%
Genève-Aéroport6916
 
1.3%
Olten5922
 
1.1%
Other values (15975)379817
71.2%

fg_startort_uic
Categorical

HIGH CARDINALITY

Distinct14300
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
 
39339
8503000
 
32807
8507000
 
21956
8500010
 
17724
8503016
 
16322
Other values (14295)
404958 

Unique

Unique3783 ?
Unique (%)0.7%

Sample

1st row8590841
2nd row8505069
3rd row8500126
4th row8507000
5th row8503428

Common Values

ValueCountFrequency (%)
-6639339
 
7.4%
850300032807
 
6.2%
850700021956
 
4.1%
850001017724
 
3.3%
850301616322
 
3.1%
850500013351
 
2.5%
850100812286
 
2.3%
850112011499
 
2.2%
85010266692
 
1.3%
85002185678
 
1.1%
Other values (14290)355452
66.7%

fg_teilstr
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
1
207844 
2
115550 
3
105211 
-66
39339 
5
28316 
Other values (10)
36846 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1207844
39.0%
2115550
21.7%
3105211
19.7%
-6639339
 
7.4%
528316
 
5.3%
423645
 
4.4%
77455
 
1.4%
62953
 
0.6%
91823
 
0.3%
8442
 
0.1%
Other values (5)528
 
0.1%

fg_via
Categorical

HIGH CARDINALITY

Distinct49633
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
184356 
-99
62833 
<div>Zürich HB</div>
 
14819
Zürich HB
 
10045
<div>Bern</div>
 
6597
Other values (49628)
254456 

Unique

Unique33034 ?
Unique (%)6.2%

Sample

1st row<div>Schlieren, Bahnhof</div>
2nd row-66
3rd row<div>Biel/Bienne</div>
4th row<div>Spiez</div>
5th row-66

Common Values

ValueCountFrequency (%)
-66184356
34.6%
-9962833
 
11.8%
<div>Zürich HB</div>14819
 
2.8%
Zürich HB10045
 
1.9%
<div>Bern</div>6597
 
1.2%
<div>Olten</div>5176
 
1.0%
Bern4485
 
0.8%
Olten3719
 
0.7%
<div>Luzern</div>3152
 
0.6%
<div>Lausanne</div>2880
 
0.5%
Other values (49623)235044
44.1%

fg_vm
Categorical

HIGH CARDINALITY

Distinct153564
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
undefined
194598 
-66
39339 
IR 90
 
2030
IC 5
 
1935
IC 1
 
1715
Other values (153559)
293489 

Unique

Unique126825 ?
Unique (%)23.8%

Sample

1st rowundefined
2nd rowundefined
3rd rowundefined
4th rowundefined
5th rowundefined

Common Values

ValueCountFrequency (%)
undefined194598
36.5%
-6639339
 
7.4%
IR 902030
 
0.4%
IC 51935
 
0.4%
IC 11715
 
0.3%
IR 151166
 
0.2%
IC 81147
 
0.2%
S 5998
 
0.2%
S 1967
 
0.2%
S 3862
 
0.2%
Other values (153554)288349
54.1%

fg_zielort
Categorical

HIGH CARDINALITY

Distinct15776
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Zürich HB
 
41159
Bern
 
25675
Basel SBB
 
17602
Luzern
 
15554
Zürich Flughafen
 
14422
Other values (15771)
418694 

Unique

Unique4657 ?
Unique (%)0.9%

Sample

1st rowWinterthur
2nd rowArth-Goldau RB
3rd rowLausanne
4th rowFaulensee, Dorf
5th rowStein am Rhein

Common Values

ValueCountFrequency (%)
Zürich HB41159
 
7.7%
Bern25675
 
4.8%
Basel SBB17602
 
3.3%
Luzern15554
 
2.9%
Zürich Flughafen14422
 
2.7%
Lausanne13584
 
2.5%
Genève12391
 
2.3%
-669494
 
1.8%
Winterthur7020
 
1.3%
St. Gallen6990
 
1.3%
Other values (15766)369215
69.3%

fg_zielort_uic
Categorical

HIGH CARDINALITY

Distinct14065
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
8503000
 
39484
-66
 
39339
8507000
 
24834
8500010
 
16784
8505000
 
14953
Other values (14060)
397712 

Unique

Unique3974 ?
Unique (%)0.7%

Sample

1st row8506000
2nd row8505063
3rd row8501120
4th row8588606
5th row8506139

Common Values

ValueCountFrequency (%)
850300039484
 
7.4%
-6639339
 
7.4%
850700024834
 
4.7%
850001016784
 
3.1%
850500014953
 
2.8%
850301614019
 
2.6%
850112012968
 
2.4%
850100811730
 
2.2%
85063026784
 
1.3%
85060006685
 
1.3%
Other values (14055)345526
64.8%

ft_abfahrt
Categorical

HIGH CARDINALITY
MISSING

Distinct1394
Distinct (%)0.3%
Missing53318
Missing (%)10.0%
Memory size4.1 MiB
17:02:00
 
1910
07:32:00
 
1752
17:04:00
 
1731
17:34:00
 
1689
16:02:00
 
1647
Other values (1389)
471059 

Unique

Unique45 ?
Unique (%)< 0.1%

Sample

1st row10:04:00
2nd row17:04:00
3rd row17:45:00
4th row13:06:00
5th row17:43:00

Common Values

ValueCountFrequency (%)
17:02:001910
 
0.4%
07:32:001752
 
0.3%
17:04:001731
 
0.3%
17:34:001689
 
0.3%
16:02:001647
 
0.3%
17:32:001608
 
0.3%
17:00:001606
 
0.3%
07:34:001583
 
0.3%
16:32:001579
 
0.3%
16:34:001560
 
0.3%
Other values (1384)463123
86.9%
(Missing)53318
 
10.0%

ft_ankunft
Categorical

HIGH CARDINALITY
MISSING

Distinct1406
Distinct (%)0.3%
Missing53318
Missing (%)10.0%
Memory size4.1 MiB
18:28:00
 
1921
17:56:00
 
1920
07:56:00
 
1793
17:28:00
 
1769
16:56:00
 
1649
Other values (1401)
470736 

Unique

Unique45 ?
Unique (%)< 0.1%

Sample

1st row10:39:00
2nd row17:50:00
3rd row18:45:00
4th row13:34:00
5th row17:55:00

Common Values

ValueCountFrequency (%)
18:28:001921
 
0.4%
17:56:001920
 
0.4%
07:56:001793
 
0.3%
17:28:001769
 
0.3%
16:56:001649
 
0.3%
08:56:001618
 
0.3%
17:58:001611
 
0.3%
18:00:001608
 
0.3%
08:28:001604
 
0.3%
08:24:001554
 
0.3%
Other values (1396)462741
86.8%
(Missing)53318
 
10.0%

ft_haltestellen
Categorical

HIGH CARDINALITY
MISSING

Distinct25028
Distinct (%)6.8%
Missing165697
Missing (%)31.1%
Memory size4.1 MiB
-66
53293 
Bern - Zürich HB
 
3796
Zürich HB - Bern
 
3722
Basel SBB - Zürich HB
 
3020
Zürich Flughafen - Zürich HB
 
2396
Other values (25023)
301182 

Unique

Unique9747 ?
Unique (%)2.7%

Sample

1st rowSchlieren - Zürich Altstetten - Zürich Hardbrücke - Zürich HB - Zürich Stadelhofen - Stettbach - Winterthur
2nd rowRigi Kulm - Rigi Staffel - Rigi Wölfertschen-First - Rigi Klösterli - Fruttli - Kräbel - Goldau A4 - Arth-Goldau RB
3rd rowBiel/Bienne - Neuchâtel - Yverdon-les-Bains - Lausanne
4th rowBern - Thun - Spiez
5th rowDiessenhofen - Schlattingen - Etzwilen - Stein am Rhein

Common Values

ValueCountFrequency (%)
-6653293
 
10.0%
Bern - Zürich HB3796
 
0.7%
Zürich HB - Bern3722
 
0.7%
Basel SBB - Zürich HB3020
 
0.6%
Zürich Flughafen - Zürich HB2396
 
0.4%
Zürich HB - Basel SBB2207
 
0.4%
Chur - Landquart - Sargans - Zürich HB1804
 
0.3%
Landquart - Sargans - Zürich HB1710
 
0.3%
Zürich HB - Sargans - Landquart - Chur1494
 
0.3%
Olten - Bern1319
 
0.2%
Other values (25018)292648
54.9%
(Missing)165697
31.1%

ft_startort
Categorical

HIGH CARDINALITY

Distinct7595
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Zürich HB
49218 
Bern
 
29908
-66
 
23513
Basel SBB
 
20497
Zürich Flughafen
 
17593
Other values (7590)
392377 

Unique

Unique3345 ?
Unique (%)0.6%

Sample

1st rowSchlieren, Bahnhof
2nd rowRigi Kulm
3rd rowBiel/Bienne
4th rowBern
5th rowDiessenhofen

Common Values

ValueCountFrequency (%)
Zürich HB49218
 
9.2%
Bern29908
 
5.6%
-6623513
 
4.4%
Basel SBB20497
 
3.8%
Zürich Flughafen17593
 
3.3%
Luzern16424
 
3.1%
Lausanne15016
 
2.8%
Genève13444
 
2.5%
Olten11863
 
2.2%
Winterthur8760
 
1.6%
Other values (7585)326870
61.3%

ft_startort_uic
Categorical

HIGH CARDINALITY

Distinct2837
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
53293 
8503000
47970 
8507000
 
29162
8500010
 
19727
8503016
 
17077
Other values (2832)
365877 

Unique

Unique338 ?
Unique (%)0.1%

Sample

1st row8590786
2nd row8505069
3rd row8504300
4th row8507000
5th row8503428

Common Values

ValueCountFrequency (%)
-6653293
 
10.0%
850300047970
 
9.0%
850700029162
 
5.5%
850001019727
 
3.7%
850301617077
 
3.2%
850500015914
 
3.0%
850112014511
 
2.7%
850100812757
 
2.4%
850021811622
 
2.2%
85060008449
 
1.6%
Other values (2827)302624
56.8%

ft_tu
Categorical

HIGH CARDINALITY
MISSING

Distinct60
Distinct (%)< 0.1%
Missing165697
Missing (%)31.1%
Memory size4.1 MiB
SBB
255572 
-66
53293 
BLS
 
14029
SOB
 
8720
THU
 
8454
Other values (55)
27341 

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowSBB
2nd rowRB
3rd rowSBB
4th rowSBB
5th rowTHU

Common Values

ValueCountFrequency (%)
SBB255572
47.9%
-6653293
 
10.0%
BLS14029
 
2.6%
SOB8720
 
1.6%
THU8454
 
1.6%
RhB5860
 
1.1%
ZB4418
 
0.8%
MGB2445
 
0.5%
RA2178
 
0.4%
TPF1434
 
0.3%
Other values (50)11006
 
2.1%
(Missing)165697
31.1%

ft_uic_haltestellen
Categorical

HIGH CARDINALITY
MISSING

Distinct33100
Distinct (%)9.0%
Missing165697
Missing (%)31.1%
Memory size4.1 MiB
-66
53293 
8507000,8503000
 
2186
8503000,8507000
 
2099
8503016,8503000
 
1784
8500010,8503000
 
1699
Other values (33095)
306348 

Unique

Unique13683 ?
Unique (%)3.7%

Sample

1st row8503509,8503001,8503020,8503000,8503003,8503147,8506000
2nd row8505069,8505068,8505067,8505066,8505065,8505064,8505062,8505063
3rd row8504300,8504221,8504200,8501120
4th row8507000,8507100,8507483
5th row8503428,8503429,8506025,8506139

Common Values

ValueCountFrequency (%)
-6653293
 
10.0%
8507000,85030002186
 
0.4%
8503000,85070002099
 
0.4%
8503016,85030001784
 
0.3%
8500010,85030001699
 
0.3%
8503000, 85070001623
 
0.3%
8507000, 85030001610
 
0.3%
8500010, 85030001321
 
0.2%
8503000,85000101289
 
0.2%
8505000,8502204,8503202,8503000940
 
0.2%
Other values (33090)299565
56.2%
(Missing)165697
31.1%

ft_vm
Categorical

HIGH CARDINALITY

Distinct30662
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
53293 
IC 5
 
3706
IC 1
 
2279
IC 8
 
2212
IR 90
 
2035
Other values (30657)
469581 

Unique

Unique9263 ?
Unique (%)1.7%

Sample

1st rowS 12
2nd rowR 170
3rd rowIC 5
4th rowIC 8
5th rowS 8

Common Values

ValueCountFrequency (%)
-6653293
 
10.0%
IC 53706
 
0.7%
IC 12279
 
0.4%
IC 82212
 
0.4%
IR 902035
 
0.4%
IR 701678
 
0.3%
IC 31329
 
0.2%
IR 151232
 
0.2%
IC 611225
 
0.2%
IR 36958
 
0.2%
Other values (30652)463159
86.9%

ft_vm_code
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
3
308475 
1
137718 
-66
53293 
2
33620 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
3308475
57.9%
1137718
25.8%
-6653293
 
10.0%
233620
 
6.3%

Category Frequency Plot

2022-11-18T17:01:06.670444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

ft_vm_kurz
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
IC
139407 
IR
135172 
S
116613 
-66
53293 
RE
33617 
Other values (10)
55004 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowR
3rd rowIC
4th rowIC
5th rowS

Common Values

ValueCountFrequency (%)
IC139407
26.1%
IR135172
25.4%
S116613
21.9%
-6653293
 
10.0%
RE33617
 
6.3%
R20796
 
3.9%
EC13756
 
2.6%
ICN12605
 
2.4%
ICE6098
 
1.1%
TGV1339
 
0.3%
Other values (5)410
 
0.1%

ft_zielort
Categorical

HIGH CARDINALITY

Distinct6546
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Zürich HB
80473 
Bern
41526 
-66
 
23511
Luzern
 
21678
Lausanne
 
21149
Other values (6541)
344769 

Unique

Unique3218 ?
Unique (%)0.6%

Sample

1st rowWinterthur
2nd rowArth-Goldau RB
3rd rowLausanne
4th rowSpiez
5th rowStein am Rhein

Common Values

ValueCountFrequency (%)
Zürich HB80473
 
15.1%
Bern41526
 
7.8%
-6623511
 
4.4%
Luzern21678
 
4.1%
Lausanne21149
 
4.0%
Olten19231
 
3.6%
Basel SBB17612
 
3.3%
Genève11915
 
2.2%
Zürich Flughafen11552
 
2.2%
Winterthur10271
 
1.9%
Other values (6536)274188
51.4%

ft_zielort_uic
Categorical

HIGH CARDINALITY

Distinct1835
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
8503000
78814 
-66
53293 
8507000
40693 
8505000
 
21081
8501120
 
20534
Other values (1830)
318691 

Unique

Unique186 ?
Unique (%)< 0.1%

Sample

1st row8506000
2nd row8505063
3rd row8501120
4th row8507483
5th row8506139

Common Values

ValueCountFrequency (%)
850300078814
 
14.8%
-6653293
 
10.0%
850700040693
 
7.6%
850500021081
 
4.0%
850112020534
 
3.9%
850021819002
 
3.6%
850001016796
 
3.2%
850100811259
 
2.1%
850301611148
 
2.1%
85060009937
 
1.9%
Other values (1825)250549
47.0%

ft_zug_nr
Categorical

HIGH CARDINALITY

Distinct19555
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
53293 
5
 
4550
1
 
3065
8
 
2857
3
 
2461
Other values (19550)
466880 

Unique

Unique4474 ?
Unique (%)0.8%

Sample

1st row12
2nd row170
3rd row5
4th row8
5th row8

Common Values

ValueCountFrequency (%)
53293
 
10.0%
54550
 
0.9%
13065
 
0.6%
82857
 
0.5%
32461
 
0.5%
902048
 
0.4%
701951
 
0.4%
151799
 
0.3%
611250
 
0.2%
21249
 
0.2%
Other values (19545)458583
86.0%

Kommentar
Categorical

HIGH CARDINALITY

Distinct143382
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
276061 
-99
109790 
Nein
 
225
-
 
189
 
185
Other values (143377)
146656 

Unique

Unique142455 ?
Unique (%)26.7%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66276061
51.8%
-99109790
 
20.6%
Nein225
 
< 0.1%
-189
 
< 0.1%
185
 
< 0.1%
nein100
 
< 0.1%
Keine100
 
< 0.1%
.79
 
< 0.1%
Non76
 
< 0.1%
No70
 
< 0.1%
Other values (143372)146231
27.4%

OES_beeintraechtigung
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing204726
Missing (%)38.4%
Memory size4.1 MiB
Nein
307229 
Ja
 
11820
Weiss nicht
 
5324
-77
 
4007

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNein
2nd rowNein
3rd rowNein
4th rowNein
5th rowNein

Common Values

ValueCountFrequency (%)
Nein307229
57.6%
Ja11820
 
2.2%
Weiss nicht5324
 
1.0%
-774007
 
0.8%
(Missing)204726
38.4%

Category Frequency Plot

2022-11-18T17:01:06.754708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_grund_beeintraecht_1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
515990 
not quoted
 
9954
quoted
 
7162

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77515990
96.8%
not quoted9954
 
1.9%
quoted7162
 
1.3%

Category Frequency Plot

2022-11-18T17:01:06.824709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_grund_beeintraecht_2
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing204726
Missing (%)38.4%
Memory size4.1 MiB
-77
317022 
not quoted
 
10027
quoted
 
1331

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77317022
59.5%
not quoted10027
 
1.9%
quoted1331
 
0.2%
(Missing)204726
38.4%

Category Frequency Plot

2022-11-18T17:01:06.896204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_grund_beeintraecht_3
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
515990 
not quoted
 
13039
quoted
 
4077

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77515990
96.8%
not quoted13039
 
2.4%
quoted4077
 
0.8%

Category Frequency Plot

2022-11-18T17:01:06.963906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_grund_beeintraecht_4
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
515990 
not quoted
 
12030
quoted
 
5086

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77515990
96.8%
not quoted12030
 
2.3%
quoted5086
 
1.0%

Category Frequency Plot

2022-11-18T17:01:07.031667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_grund_beeintraecht_5
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-77
198967 
not quouted
 
4547
quoted
 
1211

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77198967
37.3%
not quouted4547
 
0.9%
quoted1211
 
0.2%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:07.104966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_grund_beeintraecht_6
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-77
198967 
not quouted
 
5322
quoted
 
436

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77198967
37.3%
not quouted5322
 
1.0%
quoted436
 
0.1%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:07.189697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_grund_beeintraecht_7
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
95491 
not quoted
 
442
quoted
 
10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7795491
 
17.9%
not quoted442
 
0.1%
quoted10
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:07.265953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_grund_beeintraecht_7_txt
Categorical

MISSING

Distinct6
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95938 
Boh
 
1
Je pense qu'on est réfugiés en Suisse, on ne peut pas nous exprimer et les sécurité ne seront pas encore discuter avec nous...
 
1
Al equipaggio comunque chi e sul bordo del binario… Gente non informata
 
1
yv<df
 
1

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695938
 
18.0%
Boh1
 
< 0.1%
Je pense qu'on est réfugiés en Suisse, on ne peut pas nous exprimer et les sécurité ne seront pas encore discuter avec nous...1
 
< 0.1%
Al equipaggio comunque chi e sul bordo del binario… Gente non informata1
 
< 0.1%
yv<df1
 
< 0.1%
Siehe Kommentar bezüglich Fantrennung1
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:07.340981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_grund_beeintraecht_other
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
515990 
not quoted
 
11033
quoted
 
6083

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77515990
96.8%
not quoted11033
 
2.1%
quoted6083
 
1.1%

Category Frequency Plot

2022-11-18T17:01:07.422310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_grund_beeintraecht_other_txt
Categorical

HIGH CARDINALITY

Distinct3643
Distinct (%)0.7%
Missing2245
Missing (%)0.4%
Memory size4.1 MiB
-66
523675 
-99
 
3381
Nichts
 
41
nichts
 
21
Rien
 
18
Other values (3638)
 
3725

Unique

Unique3590 ?
Unique (%)0.7%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66523675
98.2%
-993381
 
0.6%
Nichts41
 
< 0.1%
nichts21
 
< 0.1%
Rien18
 
< 0.1%
rien8
 
< 0.1%
Coronavirus6
 
< 0.1%
Corona6
 
< 0.1%
Ausländer5
 
< 0.1%
Komische Leute5
 
< 0.1%
Other values (3633)3695
 
0.7%
(Missing)2245
 
0.4%

OES_grund_personal_negativ_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct874
Distinct (%)0.2%
Missing95944
Missing (%)18.0%
Memory size4.1 MiB
-66
436176 
-99
 
111
Pas nécessaire
 
2
...
 
2
ACAB
 
2
Other values (869)
 
869

Unique

Unique869 ?
Unique (%)0.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66436176
81.8%
-99111
 
< 0.1%
Pas nécessaire2
 
< 0.1%
...2
 
< 0.1%
ACAB2
 
< 0.1%
Ich finde es sind zu wenig, wenn mich einer angreift geht es bestimmt 30 Sekunden oder mehr bis sie bei mir sind. Wer weiss, vtl geht es um Leben oder Tod1
 
< 0.1%
fühle mich beobachtet1
 
< 0.1%
Par expérience si j'avais eu un problème ils ne l auraient certainement pas résolu mais m'auraient envoyé à un poste.1
 
< 0.1%
Weil sie martialisch tun.1
 
< 0.1%
je n'ai jamais pris le train avec des forces de sécurité dedans sans que je sois controlé, c'est vraiment descriminatoire, j'ai toujours la sensation de représenter le symbole des profils ciblés par les forces de sécurité. Et c'est vraiment insupportable1
 
< 0.1%
Other values (864)864
 
0.2%
(Missing)95944
 
18.0%

OES_personal_bhf_start
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
not quoted
401204 
-77
72895 
quoted
59007 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted401204
75.3%
-7772895
 
13.7%
quoted59007
 
11.1%

Category Frequency Plot

2022-11-18T17:01:07.493311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_personal_bhf_ziel
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
not quoted
63764 
-77
28346 
quoted
 
3833

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted63764
 
12.0%
-7728346
 
5.3%
quoted3833
 
0.7%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:07.663497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_personal_keine
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
quoted
326335 
not quoted
133876 
-77
72895 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rowquoted
3rd rowquoted
4th rowquoted
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted326335
61.2%
not quoted133876
25.1%
-7772895
 
13.7%

Category Frequency Plot

2022-11-18T17:01:07.734723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_personal_na
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
not quoted
414195 
-77
72895 
quoted
46016 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted414195
77.7%
-7772895
 
13.7%
quoted46016
 
8.6%

Category Frequency Plot

2022-11-18T17:01:07.806654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_personal_perron_start
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
not quoted
431388 
-77
72895 
quoted
 
28823

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted431388
80.9%
-7772895
 
13.7%
quoted28823
 
5.4%

Category Frequency Plot

2022-11-18T17:01:07.877395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_personal_perron_ziel
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
not quoted
66314 
-77
28346 
quoted
 
1283

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted66314
 
12.4%
-7728346
 
5.3%
quoted1283
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:07.946325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_personal_wunsch
Categorical

MISSING

Distinct6
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
4
45778 
-77
28346 
not quoted
17774 
2
 
2048
3
 
1085

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row-77
4th row4
5th row4

Common Values

ValueCountFrequency (%)
445778
 
8.6%
-7728346
 
5.3%
not quoted17774
 
3.3%
22048
 
0.4%
31085
 
0.2%
quoted912
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:08.023958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

OES_personal_zug
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
not quoted
448659 
-77
72895 
quoted
 
11552

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted448659
84.2%
-7772895
 
13.7%
quoted11552
 
2.2%

Category Frequency Plot

2022-11-18T17:01:08.098929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Ortskundigkeit
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
28357 
3-12mal
23458 
nur dieses Mal
15014 
1-2mal
13270 
2-5mal pro Monat
8364 
Other values (2)
7480 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2-5mal pro Monat
2nd row(fast) täglich
3rd row-77
4th row3-12mal
5th rowmehrmals pro Woche

Common Values

ValueCountFrequency (%)
-7728357
 
5.3%
3-12mal23458
 
4.4%
nur dieses Mal15014
 
2.8%
1-2mal13270
 
2.5%
2-5mal pro Monat8364
 
1.6%
mehrmals pro Woche5109
 
1.0%
(fast) täglich2371
 
0.4%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:08.178767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

participant_id
Categorical

HIGH CARDINALITY
UNIQUE

Distinct533106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
1
 
1
401220
 
1
401218
 
1
401217
 
1
401216
 
1
Other values (533101)
533101 

Unique

Unique533106 ?
Unique (%)100.0%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5

Common Values

ValueCountFrequency (%)
11
 
< 0.1%
4012201
 
< 0.1%
4012181
 
< 0.1%
4012171
 
< 0.1%
4012161
 
< 0.1%
4012151
 
< 0.1%
4012141
 
< 0.1%
4012131
 
< 0.1%
4012121
 
< 0.1%
4012111
 
< 0.1%
Other values (533096)533096
> 99.9%

project
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-1
165697 
110723
162683 
212566
95944 
184113
69799 
174695
38983 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row110723
2nd row110723
3rd row110723
4th row110723
5th row110723

Common Values

ValueCountFrequency (%)
-1165697
31.1%
110723162683
30.5%
21256695944
18.0%
18411369799
13.1%
17469538983
 
7.3%

Category Frequency Plot

2022-11-18T17:01:08.273377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

projectLfn
Categorical

HIGH CARDINALITY

Distinct224271
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
881
 
5
32138
 
5
30965
 
5
30944
 
5
30888
 
5
Other values (224266)
533081 

Unique

Unique78780 ?
Unique (%)14.8%

Sample

1st row881
2nd row491
3rd row541
4th row502
5th row510

Common Values

ValueCountFrequency (%)
8815
 
< 0.1%
321385
 
< 0.1%
309655
 
< 0.1%
309445
 
< 0.1%
308885
 
< 0.1%
308725
 
< 0.1%
309895
 
< 0.1%
308145
 
< 0.1%
323675
 
< 0.1%
307655
 
< 0.1%
Other values (224261)533056
> 99.9%

R_abo_datum
Categorical

MISSING

Distinct13
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
-77
147919 
1
 
4965
2
 
3870
3
 
2308
4
 
1561
Other values (8)
 
5120

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77147919
27.7%
14965
 
0.9%
23870
 
0.7%
32308
 
0.4%
41561
 
0.3%
51296
 
0.2%
6950
 
0.2%
7771
 
0.1%
8565
 
0.1%
11541
 
0.1%
Other values (3)997
 
0.2%
(Missing)367363
68.9%

R_abo_nutzung
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
-77
145518 
Ja
17811 
Nein
 
2305
Weiss nicht
 
109

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77145518
 
27.3%
Ja17811
 
3.3%
Nein2305
 
0.4%
Weiss nicht109
 
< 0.1%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:08.364654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_abotk_klasse
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
-77
148461 
2. Klasse
 
14498
1. Klasse
 
2570
1. und 2. Klasse
 
214

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77148461
27.8%
2. Klasse14498
 
2.7%
1. Klasse2570
 
0.5%
1. und 2. Klasse214
 
< 0.1%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:08.445526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_anschluss
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
273334 
Ja
246566 
Nein
 
13204
0
 
2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th rowJa
5th row-77

Common Values

ValueCountFrequency (%)
-77273334
51.3%
Ja246566
46.3%
Nein13204
 
2.5%
02
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:08.520767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_anschluss_1
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
nicht genannt
113924 
-77
48856 
genannt
 
2963

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownicht genannt
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
nicht genannt113924
 
21.4%
-7748856
 
9.2%
genannt2963
 
0.6%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:08.600466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_anschluss_2
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
nicht genannt
115379 
-77
48856 
genannt
 
1508

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownicht genannt
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
nicht genannt115379
 
21.6%
-7748856
 
9.2%
genannt1508
 
0.3%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:08.677551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_anschluss_3
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
nicht genannt
115107 
-77
48856 
genannt
 
1780

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownicht genannt
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
nicht genannt115107
 
21.6%
-7748856
 
9.2%
genannt1780
 
0.3%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:08.755792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_fawkontrolle
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Nein
244650 
Ja
205778 
-77
63954 
Weiss nicht
 
18240
99
 
480
Other values (2)
 
4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJa
2nd rowJa
3rd rowNein
4th rowNein
5th rowNein

Common Values

ValueCountFrequency (%)
Nein244650
45.9%
Ja205778
38.6%
-7763954
 
12.0%
Weiss nicht18240
 
3.4%
99480
 
0.1%
02
 
< 0.1%
32
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:08.838107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_gastro_catering
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
469560 
not quoted
60091 
quoted
 
3455

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77469560
88.1%
not quoted60091
 
11.3%
quoted3455
 
0.6%

Category Frequency Plot

2022-11-18T17:01:08.914893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_gastro_na
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
not quoted
285761 
-77
239724 
quoted
 
7621

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd rownot quoted
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
not quoted285761
53.6%
-77239724
45.0%
quoted7621
 
1.4%

Category Frequency Plot

2022-11-18T17:01:08.983998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_gastro_nonuse
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
quoted
256951 
-77
239724 
not quoted
36431 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd rowquoted
4th rowquoted
5th row-77

Common Values

ValueCountFrequency (%)
quoted256951
48.2%
-77239724
45.0%
not quoted36431
 
6.8%

Category Frequency Plot

2022-11-18T17:01:09.054200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_gastro_sw_bistro
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
not quoted
282142 
-77
239724 
quoted
 
11240

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd rownot quoted
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
not quoted282142
52.9%
-77239724
45.0%
quoted11240
 
2.1%

Category Frequency Plot

2022-11-18T17:01:09.124322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_gastro_takeaway
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
not quoted
278945 
-77
239724 
quoted
 
14437

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd rownot quoted
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
not quoted278945
52.3%
-77239724
45.0%
quoted14437
 
2.7%

Category Frequency Plot

2022-11-18T17:01:09.198483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_grund_nonuse_1
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-77
200107 
not quouted
 
4212
quoted
 
406

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77200107
37.5%
not quouted4212
 
0.8%
quoted406
 
0.1%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:09.272813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_grund_nonuse_2
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-77
200107 
not quouted
 
4457
quoted
 
161

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77200107
37.5%
not quouted4457
 
0.8%
quoted161
 
< 0.1%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:09.346853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_grund_nonuse_3
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-77
200107 
not quouted
 
4432
quoted
 
186

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77200107
37.5%
not quouted4432
 
0.8%
quoted186
 
< 0.1%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:09.422121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_grund_nonuse_4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-77
200107 
not quouted
 
3924
quoted
 
694

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77200107
37.5%
not quouted3924
 
0.7%
quoted694
 
0.1%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:09.496795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_grund_nonuse_5
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-77
200107 
not quouted
 
2606
quoted
 
2012

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77200107
37.5%
not quouted2606
 
0.5%
quoted2012
 
0.4%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:09.571095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_grund_nonuse_5txt
Categorical

HIGH CARDINALITY
MISSING

Distinct1930
Distinct (%)0.9%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-66
202714 
Zugausfall
 
12
Krankheit
 
12
Le train a été supprimé
 
6
Easy ride
 
5
Other values (1925)
 
1976

Unique

Unique1891 ?
Unique (%)0.9%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66202714
38.0%
Zugausfall12
 
< 0.1%
Krankheit12
 
< 0.1%
Le train a été supprimé6
 
< 0.1%
Easy ride5
 
< 0.1%
Der Zug ist ausgefallen5
 
< 0.1%
Train supprimé4
 
< 0.1%
Zug ist ausgefallen4
 
< 0.1%
Le train a été annulé4
 
< 0.1%
Ich habe ein GA4
 
< 0.1%
Other values (1920)1955
 
0.4%
(Missing)328381
61.6%

R_grund_nonuse_6
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-77
200107 
not quouted
 
3208
quoted
 
1410

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77200107
37.5%
not quouted3208
 
0.6%
quoted1410
 
0.3%
(Missing)328381
61.6%

Category Frequency Plot

2022-11-18T17:01:09.645929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_kb_wunsch
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
0
57273 
Nein, ich habe die Kundenbegleiterin/ den Kundenbegleiter nicht vermisst
50791 
-77
48854 
Nein, aber ich hätte mir die Präsenz einer Kundenbegleiterin oder eines Kundenbegleiters gewünscht
6987 
Ja, ich hätte gerne mit einer Kundenbegleiterin oder einem Kundenbegleiter gesprochen
 
1838

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rowNein, ich habe die Kundenbegleiterin/ den Kundenbegleiter nicht vermisst
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
057273
 
10.7%
Nein, ich habe die Kundenbegleiterin/ den Kundenbegleiter nicht vermisst50791
 
9.5%
-7748854
 
9.2%
Nein, aber ich hätte mir die Präsenz einer Kundenbegleiterin oder eines Kundenbegleiters gewünscht6987
 
1.3%
Ja, ich hätte gerne mit einer Kundenbegleiterin oder einem Kundenbegleiter gesprochen1838
 
0.3%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:09.730257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_nutzung_einfach
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing328380
Missing (%)61.6%
Memory size4.1 MiB
Ja
163259 
-77
36998 
Nein
 
4469

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJa
2nd rowJa
3rd rowJa
4th rowJa
5th rowJa

Common Values

ValueCountFrequency (%)
Ja163259
30.6%
-7736998
 
6.9%
Nein4469
 
0.8%
(Missing)328380
61.6%

Category Frequency Plot

2022-11-18T17:01:09.819657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_nutzung_retour
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing328380
Missing (%)61.6%
Memory size4.1 MiB
-77
193378 
Ja, ich habe das Billett für die Hin- und Rückfahrt genutzt
 
11023
Nein, ich habe das Billett nur für die Hinfahrt genutzt
 
172
Nein, ich habe das Billett weder für die Hin- noch für die Rückfahrt genutzt
 
136
Nein, ich habe das Billett nur für die Rückfahrt genutzt
 
17

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77193378
36.3%
Ja, ich habe das Billett für die Hin- und Rückfahrt genutzt11023
 
2.1%
Nein, ich habe das Billett nur für die Hinfahrt genutzt172
 
< 0.1%
Nein, ich habe das Billett weder für die Hin- noch für die Rückfahrt genutzt136
 
< 0.1%
Nein, ich habe das Billett nur für die Rückfahrt genutzt17
 
< 0.1%
(Missing)328380
61.6%

Category Frequency Plot

2022-11-18T17:01:10.038708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_nutzung_tk
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
-77
160318 
Ja
 
5205
Nein
 
220

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77160318
30.1%
Ja5205
 
1.0%
Nein220
 
< 0.1%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:10.131303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_Park
Categorical

HIGH CARDINALITY
MISSING

Distinct262
Distinct (%)0.3%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95675 
Zu teuer
 
4
Trop cher
 
3
zu wenig Parkplätze
 
2
Zu wenig Parkplätze
 
2
Other values (257)
 
257

Unique

Unique257 ?
Unique (%)0.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695675
 
17.9%
Zu teuer4
 
< 0.1%
Trop cher3
 
< 0.1%
zu wenig Parkplätze2
 
< 0.1%
Zu wenig Parkplätze2
 
< 0.1%
The short term paking was very expensiv. The cheaper one was completely full. A bigger l^parking lot should be put in place1
 
< 0.1%
Die Vorfahrt zum Bahnhof Aarau, wenn man zum Bahnhof gebracht wird mit dem Auto bzw. jemanden bringen will, ist komplett ungenügend. Viel zu wenig Platz, kaum Wendemöglichkeiten, riesiges Durcheinander.1
 
< 0.1%
Il y a que 50 places p+r à proxmité direct de la gare, et a 6h15 il n'y a plus de places1
 
< 0.1%
Parcheggi molto lontani dall'entrata in stazione. La maggior parte di quelli più vicini sono occupati dalle ditte di noleggio penalizzando i passeggeri che lasciano l'auto. Per circa sei ore 14 franchi è troppo in un parcheggio di una stazione, con un prezzo del biglietto caro. Abbiamo chiesto al bigliettaio se si avesse potuto avere una riduzione sul parcheggio, ma non lo sapeva.1
 
< 0.1%
Sehr schlecht signalisiert. Relativ weit weg. In der nacht möchte ich nicht alleine zum. Parkplatz laufen.1
 
< 0.1%
Other values (252)252
 
< 0.1%
(Missing)437163
82.0%

R_platz_andere
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92360 
not quoted
 
2959
quoted
 
624

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792360
 
17.3%
not quoted2959
 
0.6%
quoted624
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.199299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_platz_andere_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct581
Distinct (%)0.6%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95319 
Baustelle
 
17
Travaux
 
11
Passerelle
 
5
Umbau
 
4
Other values (576)
 
587

Unique

Unique567 ?
Unique (%)0.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695319
 
17.9%
Baustelle17
 
< 0.1%
Travaux11
 
< 0.1%
Passerelle5
 
< 0.1%
Umbau4
 
< 0.1%
Lift4
 
< 0.1%
Treppen2
 
< 0.1%
Passarelle zu eng2
 
< 0.1%
Auf der Passarelle2
 
< 0.1%
Manque de bancs2
 
< 0.1%
Other values (571)575
 
0.1%
(Missing)437163
82.0%

R_platz_gebauede
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92360 
not quoted
 
2998
quoted
 
585

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792360
 
17.3%
not quoted2998
 
0.6%
quoted585
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.265462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_platz_perron_eng
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92360 
not quoted
 
2163
quoted
 
1420

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792360
 
17.3%
not quoted2163
 
0.4%
quoted1420
 
0.3%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.331982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_platz_perron_leute
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92360 
quoted
 
2061
not quoted
 
1522

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792360
 
17.3%
quoted2061
 
0.4%
not quoted1522
 
0.3%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.400015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_platz_unterf_eng
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92360 
not quoted
 
2683
quoted
 
900

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792360
 
17.3%
not quoted2683
 
0.5%
quoted900
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.466798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_platz_unterf_leute
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92360 
not quoted
 
1924
quoted
 
1659

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792360
 
17.3%
not quoted1924
 
0.4%
quoted1659
 
0.3%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.532139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_platz_vorbhf
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92360 
not quoted
 
3005
quoted
 
578

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792360
 
17.3%
not quoted3005
 
0.6%
quoted578
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.599485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_platz_warte
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92360 
not quoted
 
3190
quoted
 
393

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792360
 
17.3%
not quoted3190
 
0.6%
quoted393
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.664536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_sauber_anderes
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93236 
not quoted
 
2426
quoted
 
281

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-7793236
 
17.5%
not quoted2426
 
0.5%
quoted281
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.729379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_sauber_anderes_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct272
Distinct (%)0.3%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95662 
Lift
 
5
Baustelle
 
3
Ovunque
 
2
Nirgends
 
2
Other values (267)
 
269

Unique

Unique265 ?
Unique (%)0.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695662
 
17.9%
Lift5
 
< 0.1%
Baustelle3
 
< 0.1%
Ovunque2
 
< 0.1%
Nirgends2
 
< 0.1%
Zigarettenstummel überall2
 
< 0.1%
Treppen2
 
< 0.1%
verschmutzte Treppen und Bahnsteige durch Zigaretten und Abfall1
 
< 0.1%
Odeur de toilettes sur les rails (provenance des rails mêmes?)1
 
< 0.1%
im Bahnhofsgebäude beim Postomat (diese Umgebung war bereits mehrmals ecklig Urin und Kot)1
 
< 0.1%
Other values (262)262
 
< 0.1%
(Missing)437163
82.0%

R_sauber_gebauede
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93236 
not quoted
 
1983
quoted
 
724

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-7793236
 
17.5%
not quoted1983
 
0.4%
quoted724
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.795451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_sauber_perron
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93236 
quoted
 
1451
not quoted
 
1256

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
-7793236
 
17.5%
quoted1451
 
0.3%
not quoted1256
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.866031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_sauber_unterfuehrung
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93236 
quoted
 
1592
not quoted
 
1115

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-7793236
 
17.5%
quoted1592
 
0.3%
not quoted1115
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.931966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_sauber_vorbhf
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93236 
not quoted
 
1704
quoted
 
1003

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-7793236
 
17.5%
not quoted1704
 
0.3%
quoted1003
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:10.996964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_sauber_warte
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93236 
not quoted
 
1988
quoted
 
719

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
-7793236
 
17.5%
not quoted1988
 
0.4%
quoted719
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.065071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_sauber_WC
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93236 
not quoted
 
2225
quoted
 
482

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-7793236
 
17.5%
not quoted2225
 
0.4%
quoted482
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.132652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_Sharing
Categorical

MISSING

Distinct11
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95933 
Mobility is not available there
 
1
Il n'y avait plus qu'un seul vélo électrique à sa place, et étant donné que l'unique et dernier n'était pas à sa place initiale, la carte indiquait le vélo tel ????? Ce qui signifie que l'application n'arrivait pas à le situer juste. N'ayant plus de bus, les vélo devraient être plus en quantité et surtout électrique pour rentrer rapidement eb sécurité le soir.
 
1
Kein Mobility zur Verfügung. Über Auffahrt jedoch verständlich.
 
1
Üblicherweise gibt es vormittags zu wenige Publibikes in der Velostation Schanzenstrasse
 
1
Other values (6)
 
6

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695933
 
18.0%
Mobility is not available there1
 
< 0.1%
Il n'y avait plus qu'un seul vélo électrique à sa place, et étant donné que l'unique et dernier n'était pas à sa place initiale, la carte indiquait le vélo tel ????? Ce qui signifie que l'application n'arrivait pas à le situer juste. N'ayant plus de bus, les vélo devraient être plus en quantité et surtout électrique pour rentrer rapidement eb sécurité le soir.1
 
< 0.1%
Kein Mobility zur Verfügung. Über Auffahrt jedoch verständlich.1
 
< 0.1%
Üblicherweise gibt es vormittags zu wenige Publibikes in der Velostation Schanzenstrasse1
 
< 0.1%
Kein Service bei Rent-a-Bike. Entgegen Vetsprechen bei Ausgabe in Basel gab es kein Ersatzrad für defektes E- Bike. Ausserdem langes Anstehen bei Infoschalter für Radrückgabe.1
 
< 0.1%
Es wäre wünschenswert, dass die SBB Sharingsprodukte via SBB App anbieten würde (inkl. Bezahlung)1
 
< 0.1%
Schlechter Platz, war früher viel besser als noch direkt durch Unterführung zu erreichen1
 
< 0.1%
-1
 
< 0.1%
Pas de Kiosque magasin café à côté1
 
< 0.1%
(Missing)437163
82.0%

R_stoerung
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Nein
422570 
-77
63956 
Ja
 
39845
Weiss nicht
 
6735

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNein
2nd rowNein
3rd rowNein
4th rowNein
5th rowNein

Common Values

ValueCountFrequency (%)
Nein422570
79.3%
-7763956
 
12.0%
Ja39845
 
7.5%
Weiss nicht6735
 
1.3%

Category Frequency Plot

2022-11-18T17:01:11.202390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_umsteig_andere
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92298 
not quoted
 
2507
quoted
 
1138

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792298
 
17.3%
not quoted2507
 
0.5%
quoted1138
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.275691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_umsteig_andere_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct1101
Distinct (%)1.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
94805 
Baustelle
 
8
-
 
4
Nichts
 
4
.
 
3
Other values (1096)
 
1119

Unique

Unique1078 ?
Unique (%)1.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6694805
 
17.8%
Baustelle8
 
< 0.1%
-4
 
< 0.1%
Nichts4
 
< 0.1%
.3
 
< 0.1%
Baustellensituation3
 
< 0.1%
Alles ok3
 
< 0.1%
Travaux3
 
< 0.1%
Verspätung3
 
< 0.1%
Umsteigezeit3
 
< 0.1%
Other values (1091)1104
 
0.2%
(Missing)437163
82.0%

R_umsteig_park
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92298 
not quoted
 
3488
quoted
 
157

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792298
 
17.3%
not quoted3488
 
0.7%
quoted157
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.343369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_umsteig_trambus
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92298 
not quoted
 
2936
quoted
 
709

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792298
 
17.3%
not quoted2936
 
0.6%
quoted709
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.408155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_umsteig_zug
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
92298 
quoted
 
2175
not quoted
 
1470

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7792298
 
17.3%
quoted2175
 
0.4%
not quoted1470
 
0.3%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.472617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_unzuf_comfort_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct16499
Distinct (%)3.2%
Missing11957
Missing (%)2.2%
Memory size4.1 MiB
-66
502896 
-99
 
1228
-
 
22
Altes Rollmaterial
 
20
Kein Sitzplatz
 
17
Other values (16494)
 
16966

Unique

Unique16285 ?
Unique (%)3.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66502896
94.3%
-991228
 
0.2%
-22
 
< 0.1%
Altes Rollmaterial20
 
< 0.1%
Kein Sitzplatz17
 
< 0.1%
Überfüllt15
 
< 0.1%
.13
 
< 0.1%
Nein13
 
< 0.1%
Zu voll10
 
< 0.1%
Zu wenig Sitzplätze10
 
< 0.1%
Other values (16489)16905
 
3.2%
(Missing)11957
 
2.2%

R_unzuf_fahrplan_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct6194
Distinct (%)3.7%
Missing367364
Missing (%)68.9%
Memory size4.1 MiB
-66
159490 
Verspätung
 
17
Zugausfall
 
6
Retard
 
5
Zug hatte Verspätung
 
4
Other values (6189)
 
6220

Unique

Unique6165 ?
Unique (%)3.7%

Sample

1st rowMagari fate dei sondaggi chiedendo informazioni piu logiche; avete chiesto l'ora di partenza ... che ne so io; quello che so e il ritardo al arrivo in minuti; usate il cervello e avrete piu risposte; evitate il tecnocratico che infetta le sbb
2nd row-66
3rd row-66
4th rowUmsteigen, zu volle Abteile, werde daher nicht mehr SBB benutzen, bevor die SBB mehr Abteile haben wird. Der Kondukteur könnte auch etwas freundlicher sein und den Gestapo Ton mal ablegen
5th row-66

Common Values

ValueCountFrequency (%)
-66159490
29.9%
Verspätung17
 
< 0.1%
Zugausfall6
 
< 0.1%
Retard5
 
< 0.1%
Zug hatte Verspätung4
 
< 0.1%
Mehr Verbindungen4
 
< 0.1%
Verspätung des Zuges3
 
< 0.1%
Le train avait du retard3
 
< 0.1%
Train en retard3
 
< 0.1%
Zugsausfall3
 
< 0.1%
Other values (6184)6204
 
1.2%
(Missing)367364
68.9%

R_unzuf_gastro_ambiente_txt
Categorical

HIGH CARDINALITY

Distinct144
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
367267 
165697 
Tischtuch war sehr schmutzig
 
1
Es ist alles etwas verbraucht und ältlich
 
1
Es gab keinen Speisewagen, entgegen der Sitzplatzreservation für diesen, welche ich am Vorabend am Bahnschalter machen konnte. (siehe auch vorigen Kommentar)
 
1
Other values (139)
 
139

Unique

Unique142 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66367267
68.9%
165697
31.1%
Tischtuch war sehr schmutzig1
 
< 0.1%
Es ist alles etwas verbraucht und ältlich1
 
< 0.1%
Es gab keinen Speisewagen, entgegen der Sitzplatzreservation für diesen, welche ich am Vorabend am Bahnschalter machen konnte. (siehe auch vorigen Kommentar)1
 
< 0.1%
Die ganze Einrichtung ist schlichtweg veraltet!!!1
 
< 0.1%
Automat für ältere Personen zu kompliziert, nicht selbsterklärend. Es braucht eine klare Anweisung: 1. 2. 3. etc.1
 
< 0.1%
Wenig schöne und dennoch praktische Einrichtung!1
 
< 0.1%
Ich finde die Einrichtung in den neuen Zügen (Bombardier) nicht so angenehm wie in den früheren Zügen. Es wirkt enger, das Design der Speisewageneinrichtung finde ich nicht ansprechend.1
 
< 0.1%
Zu wenig Sitzmöglichkeiten1
 
< 0.1%
Other values (134)134
 
< 0.1%

R_unzuf_gastro_auswahl_txt
Categorical

HIGH CARDINALITY

Distinct175
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
367236 
165697 
J'avais prévu de prendre mon petit déjeuner avec les enfants dans le train et il n'y avait plus que un croissant, compliqué avec 2 enfants;-)
 
1
Die Preise sind zu Hoch
 
1
Das Frühstücks-Angebot war unter aller Sau!
 
1
Other values (170)
 
170

Unique

Unique173 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66367236
68.9%
165697
31.1%
J'avais prévu de prendre mon petit déjeuner avec les enfants dans le train et il n'y avait plus que un croissant, compliqué avec 2 enfants;-)1
 
< 0.1%
Die Preise sind zu Hoch1
 
< 0.1%
Das Frühstücks-Angebot war unter aller Sau!1
 
< 0.1%
Ausverkauft1
 
< 0.1%
Die Hälfte der Gerichte auf der Karte war nicht verfügbar - es gab nur ein heisses Gericht1
 
< 0.1%
zuwenig Alternativen zur milch latte macciato mit Hafermilch ist nicht möglich, obwohl Hafermilch in der Speisekarte steht1
 
< 0.1%
Gerne Ein grosser Teil des Angebotes war nicht verfügbar (kein Frühstück, keine Gipfeli, etc.), zudem war die Kaffeemaschine erst nach 45 Minuten Fahrt einsatzbereit. Angegeben wurden logistische Gründe, ein einmaliges Malheur sozusagen. Von 10 Malen, die ich seit September auf dieser Verbindung und Uhrzeit den Spesewagen genutzt habe, ist das 3 Mal vorgekommen. Es scheint mir mehr als ein Versehen zu sein, sondern an Ihrer Logistik. Anregung: wahrscheinlich früher aufstehen:). Scherz beiseite: irgend eine Form von Sicherheit einbauen. Das schaffen Sie bestimmt.1
 
< 0.1%
Als Person die sich vegetarisch ernährt und eine Glutenunverträglichkeit hat, gibt es kaum Auswahl. Ich fahre 2x pro Woche diese Strecke, habe ich mich schon mehrfach ohne Erfolg dazu geäußert. Vielleicht hilft es diesmal. Nicht auf dieserReise aber auf anderen wurde mir klar, dass das Personal nicht geschult Ist und den Unterschied zwischen vegan und glutenfrei nicht kennt. Es ollte meiner Meinung nach zumindest glutenfreies Brot geben. Im Winter schafft man es mit Risotto, wenn dieses vegetarisch ist aber seit Monaten wird mir Bulgur (nicht glutenfrei) oder Ravioli (nicht glutenfrei) angeboten. Schade1
 
< 0.1%
Other values (165)165
 
< 0.1%

R_unzuf_gastro_freundlich_txt
Categorical

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
367366 
165697 
muffig, könnte auch an seiner Maske liegen
 
1
Sehr unfreundlich, man merkte Sie will Feierabend machen ( was ich auch verstehe ) aber wir müssen auch bis am Schluss arbeiten. Ihr war es einfach zuviel????
 
1
Zeigte null Interesse und vergass beinahe das Einkassieren…ich hätte beibahe deb Zug zu spät verlassen
 
1
Other values (40)
 
40

Unique

Unique43 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66367366
68.9%
165697
31.1%
muffig, könnte auch an seiner Maske liegen1
 
< 0.1%
Sehr unfreundlich, man merkte Sie will Feierabend machen ( was ich auch verstehe ) aber wir müssen auch bis am Schluss arbeiten. Ihr war es einfach zuviel????1
 
< 0.1%
Zeigte null Interesse und vergass beinahe das Einkassieren…ich hätte beibahe deb Zug zu spät verlassen1
 
< 0.1%
Die Bedienung war sehr unfreundlich1
 
< 0.1%
Ein Lächeln bringt ein Lächeln zurück1
 
< 0.1%
Es war ein Bistro der Deutschen Bahn... Die Service-Mitarbeiterin war sehr wortkarg (kein Danke, kein Bitte etc.).1
 
< 0.1%
They forced my companion to order, and didn’t give him enough time to decide on what he wanted.1
 
< 0.1%
Ich fand die Person ziemlich unfreundlich. An jeder Station sind Leute eingestiegen, welche eine Reservation für den Speisewagen hatte. Der Mann, hat dann einfach ziemlich gehässig die Anzahl Personen aufgefordert Platz zu machen.1
 
< 0.1%
Other values (35)35
 
< 0.1%

R_unzuf_gastro_kompetenz_txt
Categorical

HIGH CARDINALITY

Distinct68
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
367343 
165697 
Die Person war gestresst, genervt und unfreundlich
 
1
Der Herr war freundlich, angenehm und sympathisch, aber eher langsam und teilweise etwas überfordert. Für uns war das aber kein Problem.
 
1
Zuerst musste das Personal noch alles einrichten und Tischtücher montieren, bevor wir endlich fast in Winterthur etwas bestellen konnten....
 
1
Other values (63)
 
63

Unique

Unique66 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66367343
68.9%
165697
31.1%
Die Person war gestresst, genervt und unfreundlich1
 
< 0.1%
Der Herr war freundlich, angenehm und sympathisch, aber eher langsam und teilweise etwas überfordert. Für uns war das aber kein Problem.1
 
< 0.1%
Zuerst musste das Personal noch alles einrichten und Tischtücher montieren, bevor wir endlich fast in Winterthur etwas bestellen konnten....1
 
< 0.1%
La personne présente était très aimable et de bonne volonté . Mais manifestement ne semblait pas vraiment formée pour ce travail. Je me suis dit que vous deviez avoir des problèmes pour recruter des personnes qualifiées dans ce domaine .1
 
< 0.1%
Der Kellner erschien erstmals etwa drei Minuten nach Abfahrt des Zuges bei der Küche, wo er den Rolladen betätigte. Im Speisewagen waren fast alle Plätze belegt. Ich wurde dann im Hauenstein-Tunnel nach meinen Wünschen befragt.1
 
< 0.1%
Gennerell das Personal……1
 
< 0.1%
Speiseangebot nicht vorhanden. Gipfeli, Joghurt etc. nicht verfügbar. Gleiches Problem eine Woche früher Richtung Basel um 8.00 Uhr morgens! Schlecht organisiert, Personal Gleichgültig.1
 
< 0.1%
Die Dame war überaus unhöflich, inkompetent, motzig und unsymphatisch.1
 
< 0.1%
Other values (58)58
 
< 0.1%

R_unzuf_gastro_preis_txt
Categorical

HIGH CARDINALITY

Distinct249
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
367146 
165697 
Zu teuer
 
12
Überteuert
 
3
Relativ teuer
 
2
Other values (244)
 
246

Unique

Unique242 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66367146
68.9%
165697
31.1%
Zu teuer12
 
< 0.1%
Überteuert3
 
< 0.1%
Relativ teuer2
 
< 0.1%
Troppo caro2
 
< 0.1%
Viel zu teuer2
 
< 0.1%
I just bought the water at the Zurich train station from the kiosk and it was 4.80 for 500 ml. It is too expensive in my opinion. Apart from that time schedule, the cleanness and comfort of the trains are really nice and satisfactory.1
 
< 0.1%
Ein Tee in Selbstbedienung in dieser Menge ist viel zu teuer – wenn schon ein Krug. Und es brauchte eigentlich ToGo-Geschirr1
 
< 0.1%
Prices are high.1
 
< 0.1%
Other values (239)239
 
< 0.1%

R_unzuf_info_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct3333
Distinct (%)1.6%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-66
201384 
Ich war zufrieden
 
3
Ich war zufrieden!
 
3
sauberkeit
 
2
Maskenpflicht
 
2
Other values (3328)
 
3331

Unique

Unique3325 ?
Unique (%)1.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th rowLes 10 chf que j ai payé correspondent à deux réservations vélo (2 vélos) . Néanmoins je n ai pas pu réserver les places vélos que 1 jour avant . Avant cela pas de possibilité de réserver . Même la hotline m affirme qu ‘ il n était pas possible de réserver deux places pour vélos pour cause de week end de Pentecôte , information qui s’est révélée fausse
5th row-66

Common Values

ValueCountFrequency (%)
-66201384
37.8%
Ich war zufrieden3
 
< 0.1%
Ich war zufrieden!3
 
< 0.1%
sauberkeit2
 
< 0.1%
Maskenpflicht2
 
< 0.1%
?2
 
< 0.1%
-2
 
< 0.1%
Zu teuer.2
 
< 0.1%
Ich wählte eine direkte Verbindung, um nicht umsteigen zu müssen (körperliche Probleme). Sehr kurz vor Arth-Goldau wurde angekündigt, dass dort umgestiegen werden müsse, um nach Zug zu gelangen. Die Zeit, um sich anzuziehen und die Gepäckstücke im Wagen zusammenzutragen, war sehr kurz. Ich hätte mir eine frühere Durchsage gewünscht.1
 
< 0.1%
Purtroppo da quanto ho capito vi è stato un guasto del bus ed è stato necessario sostituire il bus con un ritardo sulla tabella di marcia di più di 20 minuti, ma questo sull’app non risultava, l’app mostrava esclusivamente 3 minuti di ritardo.1
 
< 0.1%
Other values (3323)3323
 
0.6%
(Missing)328381
61.6%

R_unzuf_mobile_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct5197
Distinct (%)3.1%
Missing367364
Missing (%)68.9%
Memory size4.1 MiB
-66
160345 
Kein Empfang
 
18
Kein WLAN
 
17
Schlechter Empfang
 
16
-
 
14
Other values (5192)
 
5332

Unique

Unique5118 ?
Unique (%)3.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66160345
30.1%
Kein Empfang18
 
< 0.1%
Kein WLAN17
 
< 0.1%
Schlechter Empfang16
 
< 0.1%
-14
 
< 0.1%
Nein12
 
< 0.1%
Kein wlan12
 
< 0.1%
Kein WLAN im Zug8
 
< 0.1%
Kein Wlan6
 
< 0.1%
pas de wifi5
 
< 0.1%
Other values (5187)5289
 
1.0%
(Missing)367364
68.9%

R_unzuf_platzangebot_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct8809
Distinct (%)4.3%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-66
195578 
Zu wenig Sitzplätze
 
20
Überfüllt
 
19
Der Zug war sehr voll.
 
14
Zug war überfüllt
 
14
Other values (8804)
 
9080

Unique

Unique8668 ?
Unique (%)4.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66195578
36.7%
Zu wenig Sitzplätze20
 
< 0.1%
Überfüllt19
 
< 0.1%
Der Zug war sehr voll.14
 
< 0.1%
Zug war überfüllt14
 
< 0.1%
Train bondé9
 
< 0.1%
Trop de monde9
 
< 0.1%
Beaucoup de monde8
 
< 0.1%
Zu viele Leute7
 
< 0.1%
Bereits erwähnt7
 
< 0.1%
Other values (8799)9040
 
1.7%
(Missing)328381
61.6%

R_unzuf_preis_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct12697
Distinct (%)7.7%
Missing367364
Missing (%)68.9%
Memory size4.1 MiB
-66
152371 
Zu teuer
 
163
Trop cher
 
84
zu teuer
 
62
Teuer
 
20
Other values (12692)
 
13042

Unique

Unique12553 ?
Unique (%)7.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66152371
28.6%
Zu teuer163
 
< 0.1%
Trop cher84
 
< 0.1%
zu teuer62
 
< 0.1%
Teuer20
 
< 0.1%
Viel zu teuer19
 
< 0.1%
Sehr teuer19
 
< 0.1%
trop cher17
 
< 0.1%
-12
 
< 0.1%
Prix trop élevé10
 
< 0.1%
Other values (12687)12965
 
2.4%
(Missing)367364
68.9%

R_unzuf_gastro_quality_txt
Categorical

HIGH CARDINALITY

Distinct87
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
367323 
165697 
Kaffee war kalt
 
2
Très peu de choix, rien de salé...et le sucre et un ennemi public !
 
1
Ich wollte einenKaffee und ein Gipfeli. Leider hatte es keine. Ich habe nur einen Kaffee getrunken .
 
1
Other values (82)
 
82

Unique

Unique84 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66367323
68.9%
165697
31.1%
Kaffee war kalt2
 
< 0.1%
Très peu de choix, rien de salé...et le sucre et un ennemi public !1
 
< 0.1%
Ich wollte einenKaffee und ein Gipfeli. Leider hatte es keine. Ich habe nur einen Kaffee getrunken .1
 
< 0.1%
Speisewagen war nicht bedient. Wir kamen von einem verspäteten Flug und hätten gerne etwas gegessen und getrunken. Das ist innert 6 Monaten das 2. Mal, dass mir das passiert.1
 
< 0.1%
Es kam niemand vorbei, um eine Bestellung aufzunehmen1
 
< 0.1%
Kein Kaffee oder andere Warme Getränke erhältlich!1
 
< 0.1%
There was mot faccilies to eat or drink all closed1
 
< 0.1%
Il ristorante era chiuso per un difetto alla aria condizionata, normalmente sono molto soddisfatto dal personale e del cibo. Grazie1
 
< 0.1%
Other values (77)77
 
< 0.1%

R_unzuf_puenktlichkeit_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct5552
Distinct (%)2.7%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-66
198900 
Verspätung
 
72
Retard
 
30
Zug hatte Verspätung
 
11
Retard du train
 
10
Other values (5547)
 
5702

Unique

Unique5467 ?
Unique (%)2.7%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66198900
37.3%
Verspätung72
 
< 0.1%
Retard30
 
< 0.1%
Zug hatte Verspätung11
 
< 0.1%
Retard du train10
 
< 0.1%
Train en retard10
 
< 0.1%
Zugausfall9
 
< 0.1%
Der Zug hatte Verspätung9
 
< 0.1%
Anschluss verpasst8
 
< 0.1%
Bereits erwähnt7
 
< 0.1%
Other values (5542)5659
 
1.1%
(Missing)328381
61.6%

R_unzuf_Sauberkeit_Bhf_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct2556
Distinct (%)2.3%
Missing424324
Missing (%)79.6%
Memory size4.1 MiB
-66
106193 
-
 
8
Baustelle
 
6
Abfall am Boden
 
5
.
 
4
Other values (2551)
 
2566

Unique

Unique2539 ?
Unique (%)2.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66106193
 
19.9%
-8
 
< 0.1%
Baustelle6
 
< 0.1%
Abfall am Boden5
 
< 0.1%
.4
 
< 0.1%
Littering4
 
< 0.1%
Travaux3
 
< 0.1%
Dreckig2
 
< 0.1%
Überall Müll2
 
< 0.1%
Déchets au sol2
 
< 0.1%
Other values (2546)2553
 
0.5%
(Missing)424324
79.6%

R_unzuf_sicherheit_zug
Categorical

MISSING

Distinct30
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95913 
Keine
 
2
Jeden Tag dazu lernen
 
1
this is the second time that there are loud and truly drunk passengers who disrupt other passengers. in this case between between Lausanne and Yverdon. Last time was between Lausanne and Auvernier
 
1
Zivilpolizisten, die nicht gerade freundlich mit Menschen mit Migrationshintergrund umgingen.
 
1
Other values (25)
 
25

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695913
 
18.0%
Keine2
 
< 0.1%
Jeden Tag dazu lernen1
 
< 0.1%
this is the second time that there are loud and truly drunk passengers who disrupt other passengers. in this case between between Lausanne and Yverdon. Last time was between Lausanne and Auvernier1
 
< 0.1%
Zivilpolizisten, die nicht gerade freundlich mit Menschen mit Migrationshintergrund umgingen.1
 
< 0.1%
Stesso motivo troppa gente a bordo in caso di un incidente sarebbe grave. Troppa gente in piedi in corridoio anche al bar1
 
< 0.1%
Sono razzisti1
 
< 0.1%
De voir deux malabars suivre de près les deux contrôleurs qui contrôlaient conjointement d'un bout à l'autre pour ce retrouvez au centre. Excuser moi !! avec une guelle de méchant pas de sourire je ne peux penser que devaient penser les usagés (tourisme) de ce train. Je n'avais jamais vu cela de ce sérieux de ces (faux Rambo) Je sais qu'il y a des problèmes sur cette ligne Chx de Fds - Berne mais vous n'allez pas impressionner ces jeunes à problèmes?1
 
< 0.1%
Je n'aime pas voir des uniformes sans raison. Cela provoque plus de stress que de sentiments de sécurité1
 
< 0.1%
Ich habe niemanden gesehen. Daher kann ich keine Auskunft geben1
 
< 0.1%
Other values (20)20
 
< 0.1%
(Missing)437163
82.0%

R_unzuf_stoerungsinfo_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct3767
Distinct (%)1.1%
Missing204726
Missing (%)38.4%
Memory size4.1 MiB
-66
324115 
-99
 
469
Non
 
4
aucune information
 
4
Es gab keine Information
 
4
Other values (3762)
 
3784

Unique

Unique3746 ?
Unique (%)1.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66324115
60.8%
-99469
 
0.1%
Non4
 
< 0.1%
aucune information4
 
< 0.1%
Es gab keine Information4
 
< 0.1%
Es gab keine Informationen4
 
< 0.1%
Besser informieren3
 
< 0.1%
Siehe oben3
 
< 0.1%
3
 
< 0.1%
Nein3
 
< 0.1%
Other values (3757)3768
 
0.7%
(Missing)204726
38.4%

R_unzuf_wc_avail_txt
Categorical

HIGH CARDINALITY

Distinct6012
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
525993 
-99
 
709
Kein WC vorhanden
 
22
Defekt
 
21
WC war defekt
 
12
Other values (6007)
 
6349

Unique

Unique5825 ?
Unique (%)1.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66525993
98.7%
-99709
 
0.1%
Kein WC vorhanden22
 
< 0.1%
Defekt21
 
< 0.1%
WC war defekt12
 
< 0.1%
WC war geschlossen11
 
< 0.1%
WC defekt10
 
< 0.1%
War geschlossen10
 
< 0.1%
Nein9
 
< 0.1%
Es gibt kein WC8
 
< 0.1%
Other values (6002)6301
 
1.2%

R_unzuf_wc_clean_txt
Categorical

HIGH CARDINALITY

Distinct4432
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
528038 
-99
 
502
Putzen
 
8
Schmutzig
 
7
Pas propre
 
7
Other values (4427)
 
4544

Unique

Unique4356 ?
Unique (%)0.8%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66528038
99.0%
-99502
 
0.1%
Putzen8
 
< 0.1%
Schmutzig7
 
< 0.1%
Pas propre7
 
< 0.1%
Dreckig7
 
< 0.1%
-6
 
< 0.1%
Öfters reinigen5
 
< 0.1%
Es war nicht sauber5
 
< 0.1%
Putzen!5
 
< 0.1%
Other values (4422)4516
 
0.8%

R_unzuf_Wegweisung_Bhf_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct2420
Distinct (%)2.2%
Missing424324
Missing (%)79.6%
Memory size4.1 MiB
-66
106322 
Baustelle
 
10
.
 
8
Nein
 
5
Bauarbeiten
 
5
Other values (2415)
 
2432

Unique

Unique2404 ?
Unique (%)2.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66106322
 
19.9%
Baustelle10
 
< 0.1%
.8
 
< 0.1%
Nein5
 
< 0.1%
Bauarbeiten5
 
< 0.1%
Umbau4
 
< 0.1%
Idem3
 
< 0.1%
-3
 
< 0.1%
Travaux3
 
< 0.1%
Unübersichtlich3
 
< 0.1%
Other values (2410)2416
 
0.5%
(Missing)424324
79.6%

R_unzuf_zug_clean_txt
Categorical

HIGH CARDINALITY

Distinct10918
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
520641 
-99
 
1245
.
 
15
Dreckige Sitze
 
13
-
 
13
Other values (10913)
 
11179

Unique

Unique10788 ?
Unique (%)2.0%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66520641
97.7%
-991245
 
0.2%
.15
 
< 0.1%
Dreckige Sitze13
 
< 0.1%
-13
 
< 0.1%
Nein13
 
< 0.1%
Schmutzige Sitze12
 
< 0.1%
Putzen10
 
< 0.1%
9
 
< 0.1%
nein8
 
< 0.1%
Other values (10908)11127
 
2.1%

R_unzuf_zugpers_txt
Categorical

HIGH CARDINALITY

Distinct3154
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
529688 
-99
 
214
Unfreundlich
 
16
Nein
 
6
unfreundlich
 
4
Other values (3149)
 
3178

Unique

Unique3125 ?
Unique (%)0.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66529688
99.4%
-99214
 
< 0.1%
Unfreundlich16
 
< 0.1%
Nein6
 
< 0.1%
unfreundlich4
 
< 0.1%
Siehe vorher3
 
< 0.1%
Keine Kontrolle3
 
< 0.1%
Ich habe kein Zugpersonal gesehen3
 
< 0.1%
No3
 
< 0.1%
Habe kein Zugpersonal gesehen3
 
< 0.1%
Other values (3144)3163
 
0.6%

R_Velo
Categorical

HIGH CARDINALITY
MISSING

Distinct584
Distinct (%)0.6%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95348 
Zu wenige
 
5
Zu wenige Abstellplätze
 
4
Zu wenig
 
3
Es hat zu wenig.
 
2
Other values (579)
 
581

Unique

Unique577 ?
Unique (%)0.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695348
 
17.9%
Zu wenige5
 
< 0.1%
Zu wenige Abstellplätze4
 
< 0.1%
Zu wenig3
 
< 0.1%
Es hat zu wenig.2
 
< 0.1%
Zu wenig Platz2
 
< 0.1%
zu wenig Plätze2
 
< 0.1%
Zu wenige, zuviel Unordnung1
 
< 0.1%
Beim Perron 18 stinkt es einfach immer extrem nach Urin. Ich weiss leider auch nicht, wie das besser verhindert werden könnte. Ich finde es einfach respektlos und völlig daneben, dass die Herren der Schöpfung das Gefühl haben, sie könnten überall pinkeln. Ich würde da Videokamera aufschalten und Bussen verteilen. Ist echt gruuuuusig!1
 
< 0.1%
Hat auf der linken Aareseite deutlich zu wenig Platz zur Verfügung1
 
< 0.1%
Other values (574)574
 
0.1%
(Missing)437163
82.0%

R_WC
Categorical

HIGH CARDINALITY
MISSING

Distinct103
Distinct (%)0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95841 
Das WC ist zu teuer und der Weg es zu erreichen ist zu weit.
 
1
Keine Möglichkeit mit Karte oder TWINT zu bezahlen. Kein Münzwechsler in der Nähe.
 
1
Ich finde den Preis von CHF 2.00 etwas unverschämt.
 
1
Payants et difficile à trouver. Propreté laissant à désirer
 
1
Other values (98)
 
98

Unique

Unique102 ?
Unique (%)0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695841
 
18.0%
Das WC ist zu teuer und der Weg es zu erreichen ist zu weit.1
 
< 0.1%
Keine Möglichkeit mit Karte oder TWINT zu bezahlen. Kein Münzwechsler in der Nähe.1
 
< 0.1%
Ich finde den Preis von CHF 2.00 etwas unverschämt.1
 
< 0.1%
Payants et difficile à trouver. Propreté laissant à désirer1
 
< 0.1%
Fr. 1.- ist zu teuer.1
 
< 0.1%
War nicht sauber und teilweise defekt1
 
< 0.1%
Das ist warscheinlich falsch von mir. Einfach zu teuer. 2.00 Fr. Ist zum pinkeln zu teuer da man in der RBS nicht aufs WC kann1
 
< 0.1%
Zu wenige WC`s und auch nicht alzu sauber.1
 
< 0.1%
1. Zu wenige WCs für diesen Publikumsverkehr. 2. Die WCs sind nicht einfach aufzufinden (Beschilderung!). 3. Frauen-WC war geschlossen! 4. Wegen 1. und 3. waren die anderen WCs völlig überfüllt und die Sauberkeit erfüllte den mitteleuropäischen Standard nicht.1
 
< 0.1%
Other values (93)93
 
< 0.1%
(Missing)437163
82.0%

R_wc_na_start
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
not quoted
67454 
-77
28337 
quoted
 
152

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted67454
 
12.7%
-7728337
 
5.3%
quoted152
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.559828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_wc_na_ziel
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
not quoted
67504 
-77
28337 
quoted
 
102

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted67504
 
12.7%
-7728337
 
5.3%
quoted102
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.629148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_wc_na_zug
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
not quoted
66555 
-77
28337 
quoted
 
1051

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted66555
 
12.5%
-7728337
 
5.3%
quoted1051
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.696760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_wc_nutzung
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Nein, hatte keinen Bedarf
395673 
-77
63935 
Ja
62672 
Nein, war nicht verfügbar
 
10395
4
 
430

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNein, hatte keinen Bedarf
2nd rowNein, war nicht verfügbar
3rd rowNein, hatte keinen Bedarf
4th rowNein, hatte keinen Bedarf
5th rowNein, hatte keinen Bedarf

Common Values

ValueCountFrequency (%)
Nein, hatte keinen Bedarf395673
74.2%
-7763935
 
12.0%
Ja62672
 
11.8%
Nein, war nicht verfügbar10395
 
1.9%
4430
 
0.1%
01
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:11.773651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_wc_start
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
not quoted
67000 
-77
28337 
quoted
 
606

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted67000
 
12.6%
-7728337
 
5.3%
quoted606
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.849983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_wc_ziel
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
not quoted
67030 
-77
28337 
quoted
 
576

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted67030
 
12.6%
-7728337
 
5.3%
quoted576
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.917502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_wc_zug
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
not quoted
56387 
-77
28337 
quoted
11219 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted56387
 
10.6%
-7728337
 
5.3%
quoted11219
 
2.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:11.986312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_weg_andere
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93687 
not quoted
 
1616
quoted
 
640

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7793687
 
17.6%
not quoted1616
 
0.3%
quoted640
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.056322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_weg_andere_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct612
Distinct (%)0.6%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95303 
Baustelle
 
10
Ausgang
 
5
Keine
 
4
Schliessfächer
 
4
Other values (607)
 
617

Unique

Unique600 ?
Unique (%)0.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695303
 
17.9%
Baustelle10
 
< 0.1%
Ausgang5
 
< 0.1%
Keine4
 
< 0.1%
Schliessfächer4
 
< 0.1%
Zum Lift3
 
< 0.1%
Ausgänge3
 
< 0.1%
Sortie3
 
< 0.1%
Rien2
 
< 0.1%
Zum Flughafen2
 
< 0.1%
Other values (602)604
 
0.1%
(Missing)437163
82.0%

R_weg_laeden
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93687 
not quoted
 
2022
quoted
 
234

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7793687
 
17.6%
not quoted2022
 
0.4%
quoted234
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.122683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_weg_park
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93687 
not quoted
 
2120
quoted
 
136

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7793687
 
17.6%
not quoted2120
 
0.4%
quoted136
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.188046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_weg_share
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93687 
not quoted
 
2156
quoted
 
100

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7793687
 
17.6%
not quoted2156
 
0.4%
quoted100
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.252855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_weg_trambus
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93687 
not quoted
 
1597
quoted
 
659

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7793687
 
17.6%
not quoted1597
 
0.3%
quoted659
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.318541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_weg_velo
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93687 
not quoted
 
2136
quoted
 
120

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7793687
 
17.6%
not quoted2136
 
0.4%
quoted120
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.384342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_weg_WC
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93687 
not quoted
 
1839
quoted
 
417

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7793687
 
17.6%
not quoted1839
 
0.3%
quoted417
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.449630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_weg_zug
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93687 
not quoted
 
1148
quoted
 
1108

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7793687
 
17.6%
not quoted1148
 
0.2%
quoted1108
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.514476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

R_zweck
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Freizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.)
115837 
Freizeitfahrt/ private Ferienreise/ alltägliche Erledigungen (z.B. Arztbesuch, Einkaufen, jmd. Abhol
75943 
Private Ferienreise (Reise mit mind. 1 Übernachtung)
69106 
Geschäftsreise
68390 
Freizeitfahrt/ private Ferienreise
59178 
Other values (9)
144652 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFreizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.)
2nd rowFreizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.)
3rd rowFreizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.)
4th rowPrivate Ferienreise (Reise mit mind. 1 Übernachtung)
5th rowFreizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.)

Common Values

ValueCountFrequency (%)
Freizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.)115837
21.7%
Freizeitfahrt/ private Ferienreise/ alltägliche Erledigungen (z.B. Arztbesuch, Einkaufen, jmd. Abhol75943
14.2%
Private Ferienreise (Reise mit mind. 1 Übernachtung)69106
13.0%
Geschäftsreise68390
12.8%
Freizeitfahrt/ private Ferienreise 59178
11.1%
Fahrt zum Arbeitsort40771
 
7.6%
Fahrt vom oder zum Arbeits-/ Ausbildungsort29733
 
5.6%
Alltägliche Erledigungen (z.B. Arztbesuch, Einkaufen, jmd. abholen)26770
 
5.0%
Fahrt zum Ausbildungsort20426
 
3.8%
alltägliche Erledigungen (z.B. Arztbesuch, Einkaufen, jmd. Abholen) 8966
 
1.7%
Other values (4)17986
 
3.4%

RF_bhf_abfahrt
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
88838 
quoted
 
4417
not quoted
 
2688

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7788838
 
16.7%
quoted4417
 
0.8%
not quoted2688
 
0.5%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.583880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_bhf_andere
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
88838 
not quoted
 
6946
quoted
 
159

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7788838
 
16.7%
not quoted6946
 
1.3%
quoted159
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.822443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_bhf_perron
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
88838 
quoted
 
5915
not quoted
 
1190

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7788838
 
16.7%
quoted5915
 
1.1%
not quoted1190
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.901641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_bhf_touch
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
88838 
not quoted
 
6933
quoted
 
172

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7788838
 
16.7%
not quoted6933
 
1.3%
quoted172
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:12.982088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_1
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)56.1%
Memory size4.1 MiB
quoted
139601 
not quoted
53710 
-77
40483 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd rownot quoted
4th rowquoted
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted139601
26.2%
not quoted53710
 
10.1%
-7740483
 
7.6%
(Missing)299312
56.1%

Category Frequency Plot

2022-11-18T17:01:13.049658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_12
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)56.1%
Memory size4.1 MiB
not quoted
164796 
-77
40483 
quoted
28515 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted164796
30.9%
-7740483
 
7.6%
quoted28515
 
5.3%
(Missing)299312
56.1%

Category Frequency Plot

2022-11-18T17:01:13.119700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_13
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)56.1%
Memory size4.1 MiB
not quoted
160975 
-77
40483 
quoted
32336 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted160975
30.2%
-7740483
 
7.6%
quoted32336
 
6.1%
(Missing)299312
56.1%

Category Frequency Plot

2022-11-18T17:01:13.193913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_14
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)56.1%
Memory size4.1 MiB
not quoted
190332 
-77
40483 
quoted
 
2979

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted190332
35.7%
-7740483
 
7.6%
quoted2979
 
0.6%
(Missing)299312
56.1%

Category Frequency Plot

2022-11-18T17:01:13.262848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_15
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
not quoted
52962 
-77
34536 
quoted
8445 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted52962
 
9.9%
-7734536
 
6.5%
quoted8445
 
1.6%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:13.330576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_2
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)56.1%
Memory size4.1 MiB
not quoted
174513 
-77
40483 
quoted
18798 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rowquoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted174513
32.7%
-7740483
 
7.6%
quoted18798
 
3.5%
(Missing)299312
56.1%

Category Frequency Plot

2022-11-18T17:01:13.402610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing395255
Missing (%)74.1%
Memory size4.1 MiB
not quoted
124021 
quoted
 
7883
-77
 
5947

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted124021
 
23.3%
quoted7883
 
1.5%
-775947
 
1.1%
(Missing)395255
74.1%

Category Frequency Plot

2022-11-18T17:01:13.474798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_6
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)56.1%
Memory size4.1 MiB
not quoted
191207 
-77
40483 
quoted
 
2104

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted191207
35.9%
-7740483
 
7.6%
quoted2104
 
0.4%
(Missing)299312
56.1%

Category Frequency Plot

2022-11-18T17:01:13.542597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_7
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)56.1%
Memory size4.1 MiB
not quoted
175241 
-77
40483 
quoted
18070 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted175241
32.9%
-7740483
 
7.6%
quoted18070
 
3.4%
(Missing)299312
56.1%

Category Frequency Plot

2022-11-18T17:01:13.609779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_8
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)56.1%
Memory size4.1 MiB
not quoted
142886 
quoted
50425 
-77
40483 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rowquoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted142886
26.8%
quoted50425
 
9.5%
-7740483
 
7.6%
(Missing)299312
56.1%

Category Frequency Plot

2022-11-18T17:01:13.677825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_keiner
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)56.1%
Memory size4.1 MiB
not quoted
172833 
-77
40483 
quoted
20478 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted172833
32.4%
-7740483
 
7.6%
quoted20478
 
3.8%
(Missing)299312
56.1%

Category Frequency Plot

2022-11-18T17:01:13.747049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_other
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)56.1%
Memory size4.1 MiB
not quoted
190497 
-77
40483 
quoted
 
2814

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted190497
35.7%
-7740483
 
7.6%
quoted2814
 
0.5%
(Missing)299312
56.1%

Category Frequency Plot

2022-11-18T17:01:13.817754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_kanal_other_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct2241
Distinct (%)1.0%
Missing299312
Missing (%)56.1%
Memory size4.1 MiB
-66
230980 
Google maps
 
93
Google Maps
 
76
google maps
 
34
ZVV App
 
25
Other values (2236)
 
2586

Unique

Unique2111 ?
Unique (%)0.9%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66230980
43.3%
Google maps93
 
< 0.1%
Google Maps76
 
< 0.1%
google maps34
 
< 0.1%
ZVV App25
 
< 0.1%
Google17
 
< 0.1%
Internet15
 
< 0.1%
Google map14
 
< 0.1%
DB App12
 
< 0.1%
Anzeige im Bus11
 
< 0.1%
Other values (2231)2517
 
0.5%
(Missing)299312
56.1%

RF_mob_andere
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
65930 
not quoted
29389 
quoted
 
624

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-7765930
 
12.4%
not quoted29389
 
5.5%
quoted624
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:13.889079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_mob_andere_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct405
Distinct (%)0.4%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95319 
Fahrplan
 
34
Easy Ride
 
20
EasyRide
 
14
Ticketkauf
 
13
Other values (400)
 
543

Unique

Unique351 ?
Unique (%)0.4%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695319
 
17.9%
Fahrplan34
 
< 0.1%
Easy Ride20
 
< 0.1%
EasyRide14
 
< 0.1%
Ticketkauf13
 
< 0.1%
Easy ride13
 
< 0.1%
Achat billet12
 
< 0.1%
Achat du billet9
 
< 0.1%
easy ride8
 
< 0.1%
Billetkauf8
 
< 0.1%
Other values (395)493
 
0.1%
(Missing)437163
82.0%

RF_mob_autom
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
65930 
not quoted
25472 
quoted
 
4541

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-7765930
 
12.4%
not quoted25472
 
4.8%
quoted4541
 
0.9%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:13.960797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_mob_fahrplan
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
65930 
quoted
27919 
not quoted
 
2094

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
-7765930
 
12.4%
quoted27919
 
5.2%
not quoted2094
 
0.4%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:14.030531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_mob_karte
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
65930 
not quoted
28743 
quoted
 
1270

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-7765930
 
12.4%
not quoted28743
 
5.4%
quoted1270
 
0.2%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:14.099921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_webs_andere
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93334 
not quoted
 
2541
quoted
 
68

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
-7793334
 
17.5%
not quoted2541
 
0.5%
quoted68
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:14.169460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_webs_andere_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct67
Distinct (%)0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95875 
Achat du billet
 
2
Ticket
 
2
Billetkauf
 
1
10.02
 
1
Other values (62)
 
62

Unique

Unique64 ?
Unique (%)0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695875
 
18.0%
Achat du billet2
 
< 0.1%
Ticket2
 
< 0.1%
Billetkauf1
 
< 0.1%
10.021
 
< 0.1%
Easy-ride1
 
< 0.1%
Buy tickets1
 
< 0.1%
Purchase ricket1
 
< 0.1%
ticket1
 
< 0.1%
really i was looking for the direct traim from Milano to bern, it is not available, for thata I sect Lugano it isn't my first option.1
 
< 0.1%
Other values (57)57
 
< 0.1%
(Missing)437163
82.0%

RF_webs_erwsuche
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93334 
not quoted
 
2309
quoted
 
300

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
-7793334
 
17.5%
not quoted2309
 
0.4%
quoted300
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:14.241292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_webs_fahrplan
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93334 
quoted
 
2445
not quoted
 
164

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th rowquoted
5th row-77

Common Values

ValueCountFrequency (%)
-7793334
 
17.5%
quoted2445
 
0.5%
not quoted164
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:14.309037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_webs_karte
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
93334 
not quoted
 
2494
quoted
 
115

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
-7793334
 
17.5%
not quoted2494
 
0.5%
quoted115
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:14.376880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

RF_Zufallsitem1
Categorical

MISSING

Distinct21
Distinct (%)< 0.1%
Missing227748
Missing (%)42.7%
Memory size4.1 MiB
SBB Mobile App
114011 
-66
47286 
1
30054 
-77
21480 
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)
21210 
Other values (16)
71317 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSBB Mobile App
2nd rowWebseite sbb.ch
3rd rowSBB Mobile App
4th rowSBB Mobile App
5th rowSBB Mobile App

Common Values

ValueCountFrequency (%)
SBB Mobile App114011
21.4%
-6647286
 
8.9%
130054
 
5.6%
-7721480
 
4.0%
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)21210
 
4.0%
Webseite sbb.ch19395
 
3.6%
Anzeigen im Zug9127
 
1.7%
Durchsagen im Zug7802
 
1.5%
67111
 
1.3%
85153
 
1.0%
Other values (11)22729
 
4.3%
(Missing)227748
42.7%

RF_zufallsitem1_label
Categorical

HIGH CARDINALITY
MISSING

Distinct696
Distinct (%)0.3%
Missing301740
Missing (%)56.6%
Memory size4.1 MiB
-66
61408 
SBB Mobile App
46751 
SBB Mobile App (Smartphone)
28903 
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)
14212 
Appli Mobile CFF
9763 
Other values (691)
70329 

Unique

Unique599 ?
Unique (%)0.3%

Sample

1st rowSBB Mobile App (Smartphone)
2nd rowAnnonces dans le train
3rd rowAppli Mobile CFF (smartphone)
4th rowAnzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)
5th rowSBB Mobile App (Smartphone)

Common Values

ValueCountFrequency (%)
-6661408
 
11.5%
SBB Mobile App46751
 
8.8%
SBB Mobile App (Smartphone)28903
 
5.4%
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)14212
 
2.7%
Appli Mobile CFF9763
 
1.8%
Anzeigen im Zug8685
 
1.6%
Webseite sbb.ch7720
 
1.4%
Durchsagen im Zug7024
 
1.3%
Appli Mobile CFF (smartphone)5971
 
1.1%
Affichages en gare (p. ex. affichages des départs, écrans d’affichage)4871
 
0.9%
Other values (686)36058
 
6.8%
(Missing)301740
56.6%

S_AB1_GA2kl
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing204726
Missing (%)38.4%
Memory size4.1 MiB
not quoted
290685 
-77
 
28826
quoted
 
8869

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted290685
54.5%
-7728826
 
5.4%
quoted8869
 
1.7%
(Missing)204726
38.4%

Category Frequency Plot

2022-11-18T17:01:14.450144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_AB2_GA
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing328380
Missing (%)61.6%
Memory size4.1 MiB
nicht genannt
174215 
genannt
22730 
-77
 
7781

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rowgenannt
4th rownicht genannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
nicht genannt174215
32.7%
genannt22730
 
4.3%
-777781
 
1.5%
(Missing)328380
61.6%

Category Frequency Plot

2022-11-18T17:01:14.528329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_AB2_GA1kl
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing204726
Missing (%)38.4%
Memory size4.1 MiB
-77
178285 
nicht genannt
149621 
genannt
 
474

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rownicht genannt
4th rownicht genannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
-77178285
33.4%
nicht genannt149621
28.1%
genannt474
 
0.1%
(Missing)204726
38.4%

Category Frequency Plot

2022-11-18T17:01:14.604389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_AB3_HTA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
quoted
407588 
not quoted
88911 
-77
 
36607

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd rowquoted
4th rowquoted
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted407588
76.5%
not quoted88911
 
16.7%
-7736607
 
6.9%

Category Frequency Plot

2022-11-18T17:01:14.675821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_AB4
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
nicht genannt
479338 
-77
 
36607
genannt
 
17161

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rownicht genannt
4th rownicht genannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
nicht genannt479338
89.9%
-7736607
 
6.9%
genannt17161
 
3.2%

Category Frequency Plot

2022-11-18T17:01:14.743516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_AB5
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
nicht genannt
451635 
genannt
 
44864
-77
 
36607

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rownicht genannt
4th rowgenannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
nicht genannt451635
84.7%
genannt44864
 
8.4%
-7736607
 
6.9%

Category Frequency Plot

2022-11-18T17:01:14.810778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_AB6
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
491116 
nicht genannt
 
37175
genannt
 
4815

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77491116
92.1%
nicht genannt37175
 
7.0%
genannt4815
 
0.9%

Category Frequency Plot

2022-11-18T17:01:14.880682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_AB7
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
nicht genannt
481638 
-77
 
36607
genannt
 
14861

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rownicht genannt
4th rownicht genannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
nicht genannt481638
90.3%
-7736607
 
6.9%
genannt14861
 
2.8%

Category Frequency Plot

2022-11-18T17:01:14.949923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_AB7txt
Categorical

HIGH CARDINALITY

Distinct4719
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
373457 
-99
145837 
Juniorkarte
 
2958
Juniorkarten
 
572
Junior
 
259
Other values (4714)
 
10023

Unique

Unique4044 ?
Unique (%)0.8%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66373457
70.1%
-99145837
 
27.4%
Juniorkarte2958
 
0.6%
Juniorkarten572
 
0.1%
Junior259
 
< 0.1%
Carte junior258
 
< 0.1%
Enkelkarte238
 
< 0.1%
juniorkarte175
 
< 0.1%
Velo GA149
 
< 0.1%
FVP-GA144
 
< 0.1%
Other values (4709)9059
 
1.7%

S_AB8
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
nicht genannt
450613 
genannt
45886 
-77
 
36607

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rownicht genannt
4th rownicht genannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
nicht genannt450613
84.5%
genannt45886
 
8.6%
-7736607
 
6.9%

Category Frequency Plot

2022-11-18T17:01:15.019371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_alter
Categorical

HIGH CARDINALITY

Distinct91
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-66
45166 
50
 
13868
55
 
12788
60
 
11867
52
 
11755
Other values (86)
437662 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6645166
 
8.5%
5013868
 
2.6%
5512788
 
2.4%
6011867
 
2.2%
5211755
 
2.2%
5311325
 
2.1%
5411132
 
2.1%
5611096
 
2.1%
5710893
 
2.0%
5810772
 
2.0%
Other values (81)382444
71.7%

S_berufstaetigkeit
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
ja
107245 
nein
51629 
-77
 
6869

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownein
2nd rownein
3rd rowja
4th rowja
5th rownein

Common Values

ValueCountFrequency (%)
ja107245
 
20.1%
nein51629
 
9.7%
-776869
 
1.3%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:15.095694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_sex
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
weiblich
270742 
männlich
253618 
-77
 
7780
divers
 
963
0
 
3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowweiblich
2nd rowweiblich
3rd rowweiblich
4th rowweiblich
5th rowmännlich

Common Values

ValueCountFrequency (%)
weiblich270742
50.8%
männlich253618
47.6%
-777780
 
1.5%
divers963
 
0.2%
03
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:15.172183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_sprache
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Deutsch
383765 
Français
98776 
English
 
30893
Italiano
 
19650
0
 
21

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDeutsch
2nd rowDeutsch
3rd rowFrançais
4th rowDeutsch
5th rowDeutsch

Common Values

ValueCountFrequency (%)
Deutsch383765
72.0%
Français98776
 
18.5%
English30893
 
5.8%
Italiano19650
 
3.7%
021
 
< 0.1%
-771
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:15.252645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_Usertyp1
Categorical

MISSING

Distinct8
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
-77
58476 
mehrmals pro Woche
24425 
nie
22237 
3-12mal jährlich
18243 
(fast) täglich
17704 
Other values (3)
24658 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row2-5mal pro Monat
4th rownie
5th row-77

Common Values

ValueCountFrequency (%)
-7758476
 
11.0%
mehrmals pro Woche24425
 
4.6%
nie22237
 
4.2%
3-12mal jährlich18243
 
3.4%
(fast) täglich17704
 
3.3%
2-5mal pro Monat17329
 
3.3%
1-2mal jährlich7325
 
1.4%
04
 
< 0.1%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:15.347985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_Usertyp2
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
-77
114111 
2-5mal pro Monat
20641 
3-12mal jährlich
18006 
mehrmals pro Woche
 
9904
(fast) täglich
 
1741
Other values (2)
 
1340

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownie
2nd row3-12mal jährlich
3rd row-77
4th row-77
5th row1-2mal jährlich

Common Values

ValueCountFrequency (%)
-77114111
 
21.4%
2-5mal pro Monat20641
 
3.9%
3-12mal jährlich18006
 
3.4%
mehrmals pro Woche9904
 
1.9%
(fast) täglich1741
 
0.3%
1-2mal jährlich1137
 
0.2%
nie203
 
< 0.1%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:15.448960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_wohnsitz
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
In der Schweiz / Liechtenstein
499929 
In einem anderen Land
 
25396
-77
 
7780
0
 
1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowIn der Schweiz / Liechtenstein
2nd rowIn der Schweiz / Liechtenstein
3rd rowIn der Schweiz / Liechtenstein
4th rowIn der Schweiz / Liechtenstein
5th rowIn der Schweiz / Liechtenstein

Common Values

ValueCountFrequency (%)
In der Schweiz / Liechtenstein499929
93.8%
In einem anderen Land25396
 
4.8%
-777780
 
1.5%
01
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:15.535819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

S_Usertyp3
Categorical

MISSING

Distinct8
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
-77
58496 
2-5mal pro Monat
40079 
3-12mal jährlich
39164 
mehrmals pro Woche
14629 
1-2mal jährlich
6124 
Other values (3)
7251 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row2-5mal pro Monat
4th row1-2mal jährlich
5th row-77

Common Values

ValueCountFrequency (%)
-7758496
 
11.0%
2-5mal pro Monat40079
 
7.5%
3-12mal jährlich39164
 
7.3%
mehrmals pro Woche14629
 
2.7%
1-2mal jährlich6124
 
1.1%
nie3927
 
0.7%
(fast) täglich3322
 
0.6%
02
 
< 0.1%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:15.629520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_1_zuf
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing165748
Missing (%)31.1%
Memory size4.1 MiB
-77
361159 
10
 
1519
8
 
1005
9
 
782
7
 
657
Other values (7)
 
2236

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77361159
67.7%
101519
 
0.3%
81005
 
0.2%
9782
 
0.1%
7657
 
0.1%
5652
 
0.1%
6429
 
0.1%
4394
 
0.1%
3305
 
0.1%
1233
 
< 0.1%
Other values (2)223
 
< 0.1%
(Missing)165748
31.1%

SF_kanal_12_zuf
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing165748
Missing (%)31.1%
Memory size4.1 MiB
-77
354829 
10
 
2762
8
 
2116
9
 
1387
5
 
1316
Other values (7)
 
4948

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77354829
66.6%
102762
 
0.5%
82116
 
0.4%
91387
 
0.3%
51316
 
0.2%
71300
 
0.2%
6873
 
0.2%
4779
 
0.1%
1755
 
0.1%
3715
 
0.1%
Other values (2)526
 
0.1%
(Missing)165748
31.1%

SF_kanal_13_zuf
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing165748
Missing (%)31.1%
Memory size4.1 MiB
-77
366150 
10
 
229
8
 
173
9
 
141
5
 
128
Other values (7)
 
537

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77366150
68.7%
10229
 
< 0.1%
8173
 
< 0.1%
9141
 
< 0.1%
5128
 
< 0.1%
7119
 
< 0.1%
494
 
< 0.1%
686
 
< 0.1%
184
 
< 0.1%
376
 
< 0.1%
Other values (2)78
 
< 0.1%
(Missing)165748
31.1%

SF_kanal_14_zuf
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing165748
Missing (%)31.1%
Memory size4.1 MiB
-77
365325 
10
 
608
8
 
304
9
 
231
5
 
189
Other values (7)
 
701

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77365325
68.5%
10608
 
0.1%
8304
 
0.1%
9231
 
< 0.1%
5189
 
< 0.1%
7169
 
< 0.1%
1122
 
< 0.1%
6117
 
< 0.1%
493
 
< 0.1%
390
 
< 0.1%
Other values (2)110
 
< 0.1%
(Missing)165748
31.1%

SF_kanal_2_zuf
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing165748
Missing (%)31.1%
Memory size4.1 MiB
-77
366827 
10
 
122
9
 
86
8
 
85
7
 
59
Other values (7)
 
179

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77366827
68.8%
10122
 
< 0.1%
986
 
< 0.1%
885
 
< 0.1%
759
 
< 0.1%
550
 
< 0.1%
636
 
< 0.1%
325
 
< 0.1%
423
 
< 0.1%
118
 
< 0.1%
Other values (2)27
 
< 0.1%
(Missing)165748
31.1%

SF_kanal_4_zuf
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing165748
Missing (%)31.1%
Memory size4.1 MiB
-77
366580 
10
 
228
8
 
96
5
 
92
9
 
90
Other values (7)
 
272

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77366580
68.8%
10228
 
< 0.1%
896
 
< 0.1%
592
 
< 0.1%
990
 
< 0.1%
782
 
< 0.1%
450
 
< 0.1%
weiss nicht38
 
< 0.1%
636
 
< 0.1%
326
 
< 0.1%
Other values (2)40
 
< 0.1%
(Missing)165748
31.1%

SF_kanal_6_zuf
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing165748
Missing (%)31.1%
Memory size4.1 MiB
-77
366748 
10
 
166
5
 
74
9
 
69
8
 
61
Other values (7)
 
240

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77366748
68.8%
10166
 
< 0.1%
574
 
< 0.1%
969
 
< 0.1%
861
 
< 0.1%
748
 
< 0.1%
146
 
< 0.1%
638
 
< 0.1%
334
 
< 0.1%
weiss nicht26
 
< 0.1%
Other values (2)48
 
< 0.1%
(Missing)165748
31.1%

SF_kanal_7_zuf
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing165748
Missing (%)31.1%
Memory size4.1 MiB
-77
359252 
10
 
1766
8
 
1347
5
 
934
9
 
899
Other values (7)
 
3160

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77359252
67.4%
101766
 
0.3%
81347
 
0.3%
5934
 
0.2%
9899
 
0.2%
7853
 
0.2%
6585
 
0.1%
4479
 
0.1%
1464
 
0.1%
3422
 
0.1%
Other values (2)357
 
0.1%
(Missing)165748
31.1%

SF_kanal_8_zuf
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing165748
Missing (%)31.1%
Memory size4.1 MiB
-77
358641 
10
 
2096
8
 
1576
9
 
1028
7
 
972
Other values (7)
 
3045

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77358641
67.3%
102096
 
0.4%
81576
 
0.3%
91028
 
0.2%
7972
 
0.2%
5888
 
0.2%
6580
 
0.1%
4467
 
0.1%
1427
 
0.1%
3382
 
0.1%
Other values (2)301
 
0.1%
(Missing)165748
31.1%

SF_kanal_other_zuf
Categorical

MISSING

Distinct13
Distinct (%)< 0.1%
Missing165748
Missing (%)31.1%
Memory size4.1 MiB
-77
365333 
1
 
589
weiss nicht
 
268
10
 
234
5
 
170
Other values (8)
 
764

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77365333
68.5%
1589
 
0.1%
weiss nicht268
 
0.1%
10234
 
< 0.1%
5170
 
< 0.1%
8149
 
< 0.1%
3131
 
< 0.1%
2112
 
< 0.1%
7108
 
< 0.1%
692
 
< 0.1%
Other values (3)172
 
< 0.1%
(Missing)165748
31.1%

RF_kanal_1_Zuf
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing454847
Missing (%)85.3%
Memory size4.1 MiB
5
48701 
4
14300 
10
 
4460
3
 
2847
weiss nicht
 
2232
Other values (7)
5719 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row8
3rd row10
4th row10
5th row9

Common Values

ValueCountFrequency (%)
548701
 
9.1%
414300
 
2.7%
104460
 
0.8%
32847
 
0.5%
weiss nicht2232
 
0.4%
91823
 
0.3%
81387
 
0.3%
2876
 
0.2%
1777
 
0.1%
7613
 
0.1%
Other values (2)243
 
< 0.1%
(Missing)454847
85.3%

RF_kanal_2_Zuf
Categorical

MISSING

Distinct12
Distinct (%)0.1%
Missing524198
Missing (%)98.3%
Memory size4.1 MiB
5
4642 
4
1689 
10
714 
3
 
449
weiss nicht
 
362
Other values (7)
1052 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row8

Common Values

ValueCountFrequency (%)
54642
 
0.9%
41689
 
0.3%
10714
 
0.1%
3449
 
0.1%
weiss nicht362
 
0.1%
9346
 
0.1%
8277
 
0.1%
2140
 
< 0.1%
7116
 
< 0.1%
1110
 
< 0.1%
Other values (2)63
 
< 0.1%
(Missing)524198
98.3%

RF_kanal_4_Zuf
Categorical

MISSING

Distinct11
Distinct (%)0.4%
Missing530224
Missing (%)99.5%
Memory size4.1 MiB
5
1564 
4
581 
10
167 
3
157 
weiss nicht
 
141
Other values (6)
272 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row7
4th row10
5th row3

Common Values

ValueCountFrequency (%)
51564
 
0.3%
4581
 
0.1%
10167
 
< 0.1%
3157
 
< 0.1%
weiss nicht141
 
< 0.1%
971
 
< 0.1%
866
 
< 0.1%
251
 
< 0.1%
139
 
< 0.1%
734
 
< 0.1%
(Missing)530224
99.5%

RF_kanal_6_Zuf
Categorical

MISSING

Distinct12
Distinct (%)1.8%
Missing532423
Missing (%)99.9%
Memory size4.1 MiB
5
344 
4
117 
weiss nicht
54 
1
39 
3
39 
Other values (7)
90 

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row9
2nd row10
3rd row5
4th row10
5th row10

Common Values

ValueCountFrequency (%)
5344
 
0.1%
4117
 
< 0.1%
weiss nicht54
 
< 0.1%
139
 
< 0.1%
339
 
< 0.1%
1034
 
< 0.1%
226
 
< 0.1%
917
 
< 0.1%
85
 
< 0.1%
74
 
< 0.1%
Other values (2)4
 
< 0.1%
(Missing)532423
99.9%

RF_kanal_7_Zuf
Categorical

MISSING

Distinct12
Distinct (%)0.3%
Missing528475
Missing (%)99.1%
Memory size4.1 MiB
5
2288 
4
1245 
3
368 
weiss nicht
 
226
2
 
111
Other values (7)
393 

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row9
4th row6
5th row7

Common Values

ValueCountFrequency (%)
52288
 
0.4%
41245
 
0.2%
3368
 
0.1%
weiss nicht226
 
< 0.1%
2111
 
< 0.1%
1110
 
< 0.1%
1095
 
< 0.1%
860
 
< 0.1%
949
 
< 0.1%
748
 
< 0.1%
Other values (2)31
 
< 0.1%
(Missing)528475
99.1%

RF_kanal_8_Zuf
Categorical

MISSING

Distinct14
Distinct (%)0.1%
Missing516570
Missing (%)96.9%
Memory size4.1 MiB
5
9850 
4
3782 
3
 
707
10
 
669
weiss nicht
 
313
Other values (9)
1215 

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row10
4th row10
5th row8

Common Values

ValueCountFrequency (%)
59850
 
1.8%
43782
 
0.7%
3707
 
0.1%
10669
 
0.1%
weiss nicht313
 
0.1%
9311
 
0.1%
8305
 
0.1%
2241
 
< 0.1%
1167
 
< 0.1%
7132
 
< 0.1%
Other values (4)59
 
< 0.1%
(Missing)516570
96.9%

RF_kanal_12_Zuf
Categorical

MISSING

Distinct12
Distinct (%)0.1%
Missing524394
Missing (%)98.4%
Memory size4.1 MiB
5
4832 
4
2321 
3
540 
weiss nicht
 
355
10
 
175
Other values (7)
489 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row8
3rd row7
4th row8
5th row10

Common Values

ValueCountFrequency (%)
54832
 
0.9%
42321
 
0.4%
3540
 
0.1%
weiss nicht355
 
0.1%
10175
 
< 0.1%
2163
 
< 0.1%
894
 
< 0.1%
188
 
< 0.1%
978
 
< 0.1%
748
 
< 0.1%
Other values (2)18
 
< 0.1%
(Missing)524394
98.4%

RF_kanal_13_Zuf
Categorical

MISSING

Distinct12
Distinct (%)0.1%
Missing523024
Missing (%)98.1%
Memory size4.1 MiB
5
6115 
4
2414 
3
 
471
weiss nicht
 
351
10
 
270
Other values (7)
 
461

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row9
4th row10
5th row10

Common Values

ValueCountFrequency (%)
56115
 
1.1%
42414
 
0.5%
3471
 
0.1%
weiss nicht351
 
0.1%
10270
 
0.1%
8119
 
< 0.1%
2107
 
< 0.1%
9105
 
< 0.1%
160
 
< 0.1%
741
 
< 0.1%
Other values (2)29
 
< 0.1%
(Missing)523024
98.1%

RF_kanal_14_Zuf
Categorical

MISSING

Distinct11
Distinct (%)0.4%
Missing530093
Missing (%)99.4%
Memory size4.1 MiB
5
1788 
4
732 
weiss nicht
192 
3
 
166
2
 
45
Other values (6)
 
90

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10
2nd row2
3rd rowweiss nicht
4th row10
5th row7

Common Values

ValueCountFrequency (%)
51788
 
0.3%
4732
 
0.1%
weiss nicht192
 
< 0.1%
3166
 
< 0.1%
245
 
< 0.1%
1033
 
< 0.1%
126
 
< 0.1%
913
 
< 0.1%
89
 
< 0.1%
78
 
< 0.1%
(Missing)530093
99.4%

RF_kanal_24_Zuf
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing533106
Missing (%)100.0%
Memory size4.1 MiB

SF_bhf_abfahrt
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
95018 
not quoted
 
645
quoted
 
280

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7795018
 
17.8%
not quoted645
 
0.1%
quoted280
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:15.730612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_bhf_andere
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
95018 
not quoted
 
886
quoted
 
39

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7795018
 
17.8%
not quoted886
 
0.2%
quoted39
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:15.796752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_bhf_perron
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
95018 
quoted
 
720
not quoted
 
205

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7795018
 
17.8%
quoted720
 
0.1%
not quoted205
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:15.861185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_bhf_stoerung
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
95018 
not quoted
 
832
quoted
 
93

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7795018
 
17.8%
not quoted832
 
0.2%
quoted93
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:15.925783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_bhf_touch
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
95018 
not quoted
 
919
quoted
 
6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7795018
 
17.8%
not quoted919
 
0.2%
quoted6
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:15.990685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_info_art_1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
499901 
quoted
 
19333
not quoted
 
13872

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77499901
93.8%
quoted19333
 
3.6%
not quoted13872
 
2.6%

Category Frequency Plot

2022-11-18T17:01:16.058895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_info_art_2
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
499901 
quoted
 
18882
not quoted
 
14323

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77499901
93.8%
quoted18882
 
3.5%
not quoted14323
 
2.7%

Category Frequency Plot

2022-11-18T17:01:16.352144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_info_art_3
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
500050 
not quoted
 
27209
quoted
 
5847

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77500050
93.8%
not quoted27209
 
5.1%
quoted5847
 
1.1%

Category Frequency Plot

2022-11-18T17:01:16.426753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_info_art_4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing165748
Missing (%)31.1%
Memory size4.1 MiB
-77
367209 
not quoted
 
124
quoted
 
25

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77367209
68.9%
not quoted124
 
< 0.1%
quoted25
 
< 0.1%
(Missing)165748
31.1%

Category Frequency Plot

2022-11-18T17:01:16.497893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_info_art_5
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
499901 
not quoted
 
21376
quoted
 
11829

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77499901
93.8%
not quoted21376
 
4.0%
quoted11829
 
2.2%

Category Frequency Plot

2022-11-18T17:01:16.567915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_info_art_6
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
499901 
not quoted
 
19074
quoted
 
14131

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77499901
93.8%
not quoted19074
 
3.6%
quoted14131
 
2.7%

Category Frequency Plot

2022-11-18T17:01:16.634790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_info_art_7
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
514605 
quoted
 
9856
not quoted
 
8645

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77514605
96.5%
quoted9856
 
1.8%
not quoted8645
 
1.6%

Category Frequency Plot

2022-11-18T17:01:16.701369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
493439 
not quoted
 
30521
quoted
 
9146

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77493439
92.6%
not quoted30521
 
5.7%
quoted9146
 
1.7%

Category Frequency Plot

2022-11-18T17:01:16.767885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_12
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
493439 
not quoted
 
23160
quoted
 
16507

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77493439
92.6%
not quoted23160
 
4.3%
quoted16507
 
3.1%

Category Frequency Plot

2022-11-18T17:01:16.834122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_13
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
493439 
not quoted
 
37905
quoted
 
1762

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77493439
92.6%
not quoted37905
 
7.1%
quoted1762
 
0.3%

Category Frequency Plot

2022-11-18T17:01:16.904279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_14
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
-77
493439 
not quoted
 
36986
quoted
 
2681

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77493439
92.6%
not quoted36986
 
6.9%
quoted2681
 
0.5%

Category Frequency Plot

2022-11-18T17:01:16.973201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_15
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
89753 
not quoted
 
5977
quoted
 
213

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7789753
 
16.8%
not quoted5977
 
1.1%
quoted213
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:17.039436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_2
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
493438 
not quoted
 
38899
quoted
 
768

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77493438
92.6%
not quoted38899
 
7.3%
quoted768
 
0.1%
(Missing)1
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:17.107091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing95944
Missing (%)18.0%
Memory size4.1 MiB
-77
403685 
not quoted
 
32419
quoted
 
1058

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77403685
75.7%
not quoted32419
 
6.1%
quoted1058
 
0.2%
(Missing)95944
 
18.0%

Category Frequency Plot

2022-11-18T17:01:17.181461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_6
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
493438 
not quoted
 
38876
quoted
 
791

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77493438
92.6%
not quoted38876
 
7.3%
quoted791
 
0.1%
(Missing)1
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:17.253326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_7
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
493438 
not quoted
 
28685
quoted
 
10982

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77493438
92.6%
not quoted28685
 
5.4%
quoted10982
 
2.1%
(Missing)1
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:17.321392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_8
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
493438 
not quoted
 
28223
quoted
 
11444

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77493438
92.6%
not quoted28223
 
5.3%
quoted11444
 
2.1%
(Missing)1
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:17.388912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_keiner
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
493438 
not quoted
 
36568
quoted
 
3099

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77493438
92.6%
not quoted36568
 
6.9%
quoted3099
 
0.6%
(Missing)1
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:17.457516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_other
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-77
493438 
not quoted
 
36998
quoted
 
2669

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77493438
92.6%
not quoted36998
 
6.9%
quoted2669
 
0.5%
(Missing)1
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:17.528495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_kanal_other_txt
Categorical

HIGH CARDINALITY

Distinct1721
Distinct (%)0.3%
Missing863
Missing (%)0.2%
Memory size4.1 MiB
-66
517614 
-99
 
12833
Verspätung
 
29
Radio
 
10
Andere Fahrgäste
 
6
Other values (1716)
 
1751

Unique

Unique1695 ?
Unique (%)0.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66517614
97.1%
-9912833
 
2.4%
Verspätung29
 
< 0.1%
Radio10
 
< 0.1%
Andere Fahrgäste6
 
< 0.1%
Retard6
 
< 0.1%
Mitreisende5
 
< 0.1%
Durchsage im Zug4
 
< 0.1%
Ersatzzug4
 
< 0.1%
Zugverspätung3
 
< 0.1%
Other values (1711)1729
 
0.3%
(Missing)863
 
0.2%

SF_mob_andere
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
94826 
not quoted
 
1083
quoted
 
34

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7794826
 
17.8%
not quoted1083
 
0.2%
quoted34
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:17.597961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_mob_andere_txt
Categorical

MISSING

Distinct29
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-66
95909 
Fahrplan
 
4
Ticketkauf
 
3
keine
 
2
Ticket kaufen
 
1
Other values (24)
 
24

Unique

Unique25 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-6695909
 
18.0%
Fahrplan4
 
< 0.1%
Ticketkauf3
 
< 0.1%
keine2
 
< 0.1%
Ticket kaufen1
 
< 0.1%
Ticket-Kauf1
 
< 0.1%
Die Mobile-App meinte, ich erreiche noch den Schnellzug, die Durchsage im Zug war aktueller und hat auf die S-Bahn verwiesen.1
 
< 0.1%
Entsprechender Fahrplan vom Zug1
 
< 0.1%
Parfois l'affichage du ticket ou de l'abonnement ne fonctionne momentanément plus1
 
< 0.1%
Zuginformation1
 
< 0.1%
Other values (19)19
 
< 0.1%
(Missing)437163
82.0%

SF_mob_autom
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
94826 
not quoted
 
780
quoted
 
337

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7794826
 
17.8%
not quoted780
 
0.1%
quoted337
 
0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:17.664422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_mob_fahrplan
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
94826 
quoted
 
983
not quoted
 
134

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7794826
 
17.8%
quoted983
 
0.2%
not quoted134
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:17.729057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_mob_karte
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing437163
Missing (%)82.0%
Memory size4.1 MiB
-77
94826 
not quoted
 
1067
quoted
 
50

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-7794826
 
17.8%
not quoted1067
 
0.2%
quoted50
 
< 0.1%
(Missing)437163
82.0%

Category Frequency Plot

2022-11-18T17:01:17.795921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

tag_zug_nr
Categorical

HIGH CARDINALITY

Distinct406662
Distinct (%)76.3%
Missing96
Missing (%)< 0.1%
Memory size4.1 MiB
2022-08-25;
 
194
2020-06-09;
 
180
2020-07-20;
 
167
2022-07-14;
 
162
2022-08-16;
 
145
Other values (406657)
532162 

Unique

Unique361992 ?
Unique (%)67.9%

Sample

1st row2018-07-01;12
2nd row2018-07-01;170
3rd row2018-07-01;5
4th row2018-07-01;8
5th row2018-07-01;8

Common Values

ValueCountFrequency (%)
2022-08-25;194
 
< 0.1%
2020-06-09;180
 
< 0.1%
2020-07-20;167
 
< 0.1%
2022-07-14;162
 
< 0.1%
2022-08-16;145
 
< 0.1%
2022-08-11;143
 
< 0.1%
2022-09-24;137
 
< 0.1%
2022-08-10;134
 
< 0.1%
2022-08-04;132
 
< 0.1%
2022-07-27;130
 
< 0.1%
Other values (406652)531486
99.7%

u_artikel
Categorical

HIGH CARDINALITY
MISSING

Distinct246
Distinct (%)0.1%
Missing165697
Missing (%)31.1%
Memory size4.1 MiB
125
151548 
-66
74830 
52508
15319 
52509
 
10657
10972
 
8614
Other values (241)
106441 

Unique

Unique39 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
125151548
28.4%
-6674830
14.0%
5250815319
 
2.9%
5250910657
 
2.0%
109728614
 
1.6%
20077032
 
1.3%
124446187
 
1.2%
77256172
 
1.2%
40925750
 
1.1%
124485423
 
1.0%
Other values (236)75877
14.2%
(Missing)165697
31.1%

u_bezugsart
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Mobile
186923 
-66
178331 
MobileTicket
48320 
Internet
45946 
Mobile Ticket
34706 
Other values (2)
38880 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile
2nd rowMobile
3rd rowMobile
4th rowMobile
5th rowMobile

Common Values

ValueCountFrequency (%)
Mobile186923
35.1%
-66178331
33.5%
MobileTicket48320
 
9.1%
Internet45946
 
8.6%
Mobile Ticket34706
 
6.5%
Onlineticket22642
 
4.2%
-9916238
 
3.0%

Category Frequency Plot

2022-11-18T17:01:17.884444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_unzuf_info_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct1404
Distinct (%)0.7%
Missing328381
Missing (%)61.6%
Memory size4.1 MiB
-66
203313 
.
 
4
Zu späte Information
 
3
Manque de précision
 
2
siehe vorne
 
2
Other values (1399)
 
1401

Unique

Unique1397 ?
Unique (%)0.7%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66203313
38.1%
.4
 
< 0.1%
Zu späte Information3
 
< 0.1%
Manque de précision2
 
< 0.1%
siehe vorne2
 
< 0.1%
Siehe oben2
 
< 0.1%
-2
 
< 0.1%
weil ich es erst gesehen habe, nachdem wir in Bern bereits wieder Richtung Olten losgefahren sind! im Familienabteil nicht sichtbar!!!1
 
< 0.1%
Informations très générales et peu claires1
 
< 0.1%
Eine Durchsage es gibt Probleme mit einer neuen Zugkomposition bringen mir als Fahrgast nichts. Ich will wissen, kann ich meinen Anschluss erreichen. Wenn nein, wie geht es weiter.1
 
< 0.1%
Other values (1394)1394
 
0.3%
(Missing)328381
61.6%

SF_Zufallsitem1
Categorical

MISSING

Distinct8
Distinct (%)< 0.1%
Missing367358
Missing (%)68.9%
Memory size4.1 MiB
-66
157281 
Durchsagen im Zug
 
3066
SBB Mobile App
 
1757
Durchsagen am Bahnhof
 
1700
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)
 
1549
Other values (3)
 
395

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66157281
29.5%
Durchsagen im Zug3066
 
0.6%
SBB Mobile App1757
 
0.3%
Durchsagen am Bahnhof1700
 
0.3%
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)1549
 
0.3%
automatische Benachrichtigung durch SBB Mobile App (Push-Meldung, abonnierte Fahrplan-Benachrichtigu151
 
< 0.1%
Webseite sbb.ch137
 
< 0.1%
32107
 
< 0.1%
(Missing)367358
68.9%

Category Frequency Plot

2022-11-18T17:01:17.985438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

SF_Zufallsitem2
Categorical

MISSING

Distinct20
Distinct (%)< 0.1%
Missing367358
Missing (%)68.9%
Memory size4.1 MiB
-66
156754 
12
 
1891
1
 
1116
Durchsagen im Zug
 
1065
7
 
998
Other values (15)
 
3924

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66156754
29.4%
121891
 
0.4%
11116
 
0.2%
Durchsagen im Zug1065
 
0.2%
7998
 
0.2%
8923
 
0.2%
Durchsagen am Bahnhof635
 
0.1%
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)621
 
0.1%
SBB Mobile App546
 
0.1%
15215
 
< 0.1%
Other values (10)984
 
0.2%
(Missing)367358
68.9%

u_date
Categorical

HIGH CARDINALITY

Distinct2063
Distinct (%)0.4%
Missing96
Missing (%)< 0.1%
Memory size4.1 MiB
2019-07-17
 
1028
2022-09-24
 
771
2020-06-09
 
729
2018-04-11
 
644
2020-01-16
 
636
Other values (2058)
529202 

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row2018-07-01
2nd row2018-07-01
3rd row2018-07-01
4th row2018-07-01
5th row2018-07-01

Common Values

ValueCountFrequency (%)
2019-07-171028
 
0.2%
2022-09-24771
 
0.1%
2020-06-09729
 
0.1%
2018-04-11644
 
0.1%
2020-01-16636
 
0.1%
2019-01-22634
 
0.1%
2018-04-10631
 
0.1%
2022-10-05558
 
0.1%
2017-01-22552
 
0.1%
2022-08-05548
 
0.1%
Other values (2053)526279
98.7%

u_fahrart
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Einfache Fahrt
246442 
keine Fahrart (Missing)
210834 
Hin- und Rückfahrt
75830 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkeine Fahrart (Missing)
2nd rowEinfache Fahrt
3rd rowHin- und Rückfahrt
4th rowEinfache Fahrt
5th rowkeine Fahrart (Missing)

Common Values

ValueCountFrequency (%)
Einfache Fahrt246442
46.2%
keine Fahrart (Missing)210834
39.5%
Hin- und Rückfahrt75830
 
14.2%

Category Frequency Plot

2022-11-18T17:01:18.078102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

u_fahrausweis
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing367363
Missing (%)68.9%
Memory size4.1 MiB
normales Billett
127399 
GA
19405 
Sparbillett
 
12694
Spartageskarte
 
4763
Tageskarte
 
662
Other values (2)
 
820

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormales Billett
2nd rownormales Billett
3rd rownormales Billett
4th rownormales Billett
5th rownormales Billett

Common Values

ValueCountFrequency (%)
normales Billett127399
 
23.9%
GA19405
 
3.6%
Sparbillett12694
 
2.4%
Spartageskarte4763
 
0.9%
Tageskarte662
 
0.1%
Strecken-/Modulabo616
 
0.1%
seven25204
 
< 0.1%
(Missing)367363
68.9%

Category Frequency Plot

2022-11-18T17:01:18.166343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

u_ga
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing204726
Missing (%)38.4%
Memory size4.1 MiB
kein GA
299554 
besitzt GA
 
28826

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkein GA
2nd rowkein GA
3rd rowkein GA
4th rowkein GA
5th rowkein GA

Common Values

ValueCountFrequency (%)
kein GA299554
56.2%
besitzt GA28826
 
5.4%
(Missing)204726
38.4%

Category Frequency Plot

2022-11-18T17:01:18.246530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

u_hindatum
Categorical

HIGH CARDINALITY

Distinct2063
Distinct (%)0.4%
Missing2963
Missing (%)0.6%
Memory size4.1 MiB
2019-07-17
 
1028
2022-09-24
 
761
2020-06-09
 
729
2018-04-11
 
644
2020-01-16
 
636
Other values (2058)
526345 

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row2018-07-01
2nd row2018-07-01
3rd row2018-07-01
4th row2018-07-01
5th row2018-07-01

Common Values

ValueCountFrequency (%)
2019-07-171028
 
0.2%
2022-09-24761
 
0.1%
2020-06-09729
 
0.1%
2018-04-11644
 
0.1%
2020-01-16636
 
0.1%
2019-01-22634
 
0.1%
2018-04-10630
 
0.1%
2017-01-22553
 
0.1%
2022-10-05551
 
0.1%
2022-08-05543
 
0.1%
Other values (2053)523434
98.2%
(Missing)2963
 
0.6%

u_kategorie
Categorical

MISSING

Distinct6
Distinct (%)< 0.1%
Missing379465
Missing (%)71.2%
Memory size4.1 MiB
ZVS_REG
116958 
-66
28886 
ZVS_GAST
 
6789
MCS_GAST
 
627
MCS_REG
 
378

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZVS_REG
2nd rowZVS_REG
3rd rowZVS_REG
4th rowZVS_REG
5th rowZVS_REG

Common Values

ValueCountFrequency (%)
ZVS_REG116958
 
21.9%
-6628886
 
5.4%
ZVS_GAST6789
 
1.3%
MCS_GAST627
 
0.1%
MCS_REG378
 
0.1%
Gast3
 
< 0.1%
(Missing)379465
71.2%

Category Frequency Plot

2022-11-18T17:01:18.328615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

u_kaufdatum
Categorical

HIGH CARDINALITY
MISSING

Distinct2110
Distinct (%)0.4%
Missing49108
Missing (%)9.2%
Memory size4.1 MiB
2019-07-17
 
824
2020-01-15
 
810
2020-06-09
 
664
2018-04-10
 
570
2022-08-04
 
540
Other values (2105)
480590 

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st row2018-07-01
2nd row2018-07-01
3rd row2018-07-01
4th row2018-07-01
5th row2018-07-01

Common Values

ValueCountFrequency (%)
2019-07-17824
 
0.2%
2020-01-15810
 
0.2%
2020-06-09664
 
0.1%
2018-04-10570
 
0.1%
2022-08-04540
 
0.1%
2018-12-17535
 
0.1%
2017-05-24523
 
0.1%
2018-04-11512
 
0.1%
2020-01-14506
 
0.1%
2017-01-22504
 
0.1%
Other values (2100)478010
89.7%
(Missing)49108
 
9.2%

u_klassencode
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2. Klasse
445541 
1. Klasse
73242 
-77
 
11572
0
 
2188
weiss nicht / beide Klassen
 
563

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2. Klasse
2nd row2. Klasse
3rd row2. Klasse
4th row2. Klasse
5th row2. Klasse

Common Values

ValueCountFrequency (%)
2. Klasse445541
83.6%
1. Klasse73242
 
13.7%
-7711572
 
2.2%
02188
 
0.4%
weiss nicht / beide Klassen563
 
0.1%

Category Frequency Plot

2022-11-18T17:01:18.411583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

u_preis
Categorical

HIGH CARDINALITY

Distinct2839
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Memory size4.1 MiB
-66
 
32032
-99
 
16238
13.00
 
7239
6.80
 
6664
3.40
 
5681
Other values (2834)
465251 

Unique

Unique582 ?
Unique (%)0.1%

Sample

1st row7.50
2nd row22.50
3rd row56.00
4th row12.30
5th row2.90

Common Values

ValueCountFrequency (%)
-6632032
 
6.0%
-9916238
 
3.0%
13.007239
 
1.4%
6.806664
 
1.3%
3.405681
 
1.1%
3.704250
 
0.8%
8.804218
 
0.8%
28.004079
 
0.8%
6.203809
 
0.7%
14.003673
 
0.7%
Other values (2829)445222
83.5%

u_ticket
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Mobile-Ticket
369199 
Online-Ticket
81026 
keine Zuordnung / GA-Besitzer
74991 
Easy Ride
 
7416
bedienter Vertrieb
 
450

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile-Ticket
2nd rowMobile-Ticket
3rd rowMobile-Ticket
4th rowMobile-Ticket
5th rowMobile-Ticket

Common Values

ValueCountFrequency (%)
Mobile-Ticket369199
69.3%
Online-Ticket81026
 
15.2%
keine Zuordnung / GA-Besitzer74991
 
14.1%
Easy Ride7416
 
1.4%
bedienter Vertrieb450
 
0.1%
324
 
< 0.1%

Category Frequency Plot

2022-11-18T17:01:18.494794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

u_zusatz
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing54952
Missing (%)10.3%
Memory size4.1 MiB
-66
354716 
-99
110709 
1
 
10918
0
 
1811

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66354716
66.5%
-99110709
 
20.8%
110918
 
2.0%
01811
 
0.3%
(Missing)54952
 
10.3%

Category Frequency Plot

2022-11-18T17:01:18.574170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

updatedAt
Categorical

HIGH CARDINALITY

Distinct447792
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2019-10-09 15:37:25.620
 
4
2019-10-09 16:45:05.973
 
4
2019-10-09 15:59:46.923
 
4
2019-10-09 16:02:01.723
 
4
2019-10-09 15:28:20.710
 
4
Other values (447787)
533086 

Unique

Unique381783 ?
Unique (%)71.6%

Sample

1st row2019-07-19 22:15:07.430
2nd row2019-07-19 22:15:07.803
3rd row2019-07-19 22:15:07.920
4th row2019-07-19 22:15:08.033
5th row2019-07-19 22:15:08.143

Common Values

ValueCountFrequency (%)
2019-10-09 15:37:25.6204
 
< 0.1%
2019-10-09 16:45:05.9734
 
< 0.1%
2019-10-09 15:59:46.9234
 
< 0.1%
2019-10-09 16:02:01.7234
 
< 0.1%
2019-10-09 15:28:20.7104
 
< 0.1%
2019-10-09 16:53:09.2174
 
< 0.1%
2019-10-09 16:05:21.8804
 
< 0.1%
2019-10-09 16:16:32.8934
 
< 0.1%
2019-10-09 16:32:37.1804
 
< 0.1%
2019-10-09 15:36:49.5574
 
< 0.1%
Other values (447782)533066
> 99.9%

wime_stoerungsinfo
Categorical

MISSING

Distinct12
Distinct (%)< 0.1%
Missing204726
Missing (%)38.4%
Memory size4.1 MiB
-77
300516 
8
 
4716
10
 
4462
7
 
3319
9
 
3099
Other values (7)
 
12268

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77300516
56.4%
84716
 
0.9%
104462
 
0.8%
73319
 
0.6%
93099
 
0.6%
12827
 
0.5%
52461
 
0.5%
62171
 
0.4%
31525
 
0.3%
41377
 
0.3%
Other values (2)1907
 
0.4%
(Missing)204726
38.4%

wime_unzuf_sf_txt
Categorical

HIGH CARDINALITY
MISSING

Distinct1470
Distinct (%)0.9%
Missing367364
Missing (%)68.9%
Memory size4.1 MiB
-66
164246 
.
 
5
-
 
4
No
 
4
Anschluss verpasst
 
3
Other values (1465)
 
1480

Unique

Unique1452 ?
Unique (%)0.9%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66164246
30.8%
.5
 
< 0.1%
-4
 
< 0.1%
No4
 
< 0.1%
Anschluss verpasst3
 
< 0.1%
?3
 
< 0.1%
Nein3
 
< 0.1%
Es gab keine Bemühungen2
 
< 0.1%
Siehe oben2
 
< 0.1%
Alternativen aufzeigen2
 
< 0.1%
Other values (1460)1468
 
0.3%
(Missing)367364
68.9%